Our sense of body ownership results from the ongoing integration of perceptual information coming from the different senses . The Rubber Hand Illusion (RHI) has been extensively studied to investigate the malleability of body ownership through contrasting multisensory information. Indeed, during the RHI, stroking a visible rubber hand synchronously to participants’ hand hidden from sight generates the illusion of ownership of the rubber hand (embodiment) and the mis-location of participants’ hand as closer to the rubber hand (proprioceptive drift). It is well known that the RHI is optimally evoked by a pleasant stroking (affective) touch, but what of an unpleasant (painful) stroking touch – does hedonic valence matter? To this aim, participants repeated the RHI while receiving different types of touch: pleasant, painful, and neutral. Results showed, for the first time, that the subjective intensity of the tactile stimulation experienced across the different conditions modulates the strength of the proprioceptive drift. Notably, participants reported a stronger RHI (mis-placed body ownership) from stimulation rated as more intense and involving an interoceptive activation . We propose that interoceptive information, regardless of the valence of the stimuli (positive or negative), are perceived as more intense and enhance, through the activation of the limbic system, multisensory integration. In the context of the RHI, this translates to a stronger illusion in terms of proprioceptive drift. In turnThe Rubber Hand Illusion RHI;  is a wela priori semantic model of the body  and the intensity of the tactile stimulation in the different conditions, in line with classic theories of emotions defining emotional states in terms of activation  and quality  were included in the study. Participants gave written consent for participation after being informed about the study aims and procedure. The local Ethical Committee of Psychological Research (Liverpool John Moores University) approved the study protocol , in accordance with the WMA Declaration of Helsinki, 2013. A power analysis using G*Power 3.0.10 (f = 0.25) with 80% power, using a repeated-measure design with six as the number of measurements and with alpha at 0.05 (two tailed).The study was conducted at the Department of Psychology of the Liverpool John Moores University. Twenty-one young adults  between the ages of 18 and 38 years old  was recorded by means of two electrodes applied on the index and middle finger of the participant’s stimulated hand with each. The two electrodes were connected to a physiological monitoring device (Biopac MP30B-CE), which in turn was connected to a computer that recorded the physiological activity through the Biopac Student Lab Pro 3.7 software. The software was programed to filter the EDA signal in real time with a band-pass of 0–35 Hz. EDA data were recorded continuously during each of the six RHI blocks . For each block a 1 min baseline recording was obtained prior to the administration of the RHI. Each recording was visually inspected for artifacts, which were manually removed using Biopac (MP150) Systems. Skin Conductance Levels  were then extrapolated from the EDA data averaging across the EDA signal in each baseline and experimental recording. SCLs in each experimental condition were then corrected by the corresponding baseline value, giving six baseline-corrected SCLs values for each participant. SCLs have been used as an index of physiological arousal.p−value was also calculated using the “lmerTest” package  included only the random effect of Participants; the first (Model 1) included Synchrony  as fixed factor and Participants as random factor; the second (Model 2) included also the Type of Touch  as fixed factor; the third (Model 3) added the interaction between Synchrony and Type of Touch. Tables reporting the specification of model comparison can be found in 2(1) = 4.04, p = 0.044] indicated that synchronous stimulation was perceived as more pleasant compared to asynchronous stimulation. Moreover a main effect of Type of touch  and the interaction effect between Type of touch and Synchrony emerged . Specifically, contrast revealed that brush stroking is perceived as more pleasant than touch with the pinwheel and tapping, and such difference is enhanced when the tactile stimulation on the participant’s hand was administered synchronously in respect to the visual feedback on the rubber hand. On the contrary, no difference emerged between tactile stimulation with the pinwheel and tapping  = 83.10, p < 0.001]. Contrasts revealed that the tactile stimulation with the pinwheel was perceived as more painful  than brush stroking  and tapping . On the contrary, no difference emerged between tactile stimulation with the brush and tapping  = 7.65, p = 0.006] and Type of touch . These results indicate that synchronous stimulation was perceived as more intense compared to asynchronous stimulation. Moreover, contrast revealed that the three types of touch differed between each other in respect to the subjective intensity reported by participants. Specifically, pinwheel was reported as the most intense stimulation  and tapping as the least intense  = 6.00, p = 0.050]. Contrasts revealed that the tactile stimulation with the pinwheel produced an increase of SCL . On the contrary, brush stroking  and tapping  elicited a decrease of SCL , which was the dependent variable of all models. A set of four nested mixed-effects models were tested including the same factors used to analyze subjective evaluations of tactile experience. Moreover we wanted to control whether the subjective valence and intensity of the touch influenced SCL, thus we tested three additional models, including self-reported pleasantness (Model 4) and painful sensation (Model 5) and intensity (Model 6). The likelihood ratio test showed that the Model 5 was the best at predicting the collected data (descriptive statistics and model comparison are reported in e of SCL .2(1) = 230.7, p < 0.001; To assess subjective illusion experience we analyzed an embodiment score based on the first three items of the Embodiment Questionnaire, which was the dependent variable of all models. A set of four nested mixed-effects models were tested, including the same factors used to analyze subjective evaluations of tactile experience. The likelihood ratio test showed that Model 1 was the best at predicting the collected data and included the factor Synchrony (descriptive statistics and model comparison are reported in 2(1) = 9.07, p = 0.003; In order to analyze whether the illusion resulted in a shift in the proprioceptive perceived position of the participants’ hand, we calculated the difference score between post-stimulation and pre-stimulation pointing (proprioceptive drift – PD). A set of four nested mixed-effects models were tested including the same factors used to analyze subjective embodiment. The likelihood ratio test showed that Model 1 was the best at predicting the collected data and included the factor Synchrony (descriptive statistics and model comparison are reported in 2(1) = 4.01, p = 0.045] and a main effect of Intensity , indicating that the subjective perception of the tactile experience was a significant factor in modulating the proprioceptive shift  and the SCL (Model 7) as predictors, the likelihood ratio test showed that Model 6 was the best at predicting the collected data see . The modve shift .t-tests comparing the PD with the null level (zero) were separately performed for each experimental condition in order to explore whether the visual-tactile stimulation elicited a significant shift from the initial localization of the hand. The results revealed that only when the tactile stimulation on the participants’ real hand was performed with a brush or with a pinwheel in synchrony with the stimulation on the rubber hand, did participants show a significant change in the proprioceptive position of their own hand   during the paradigm elicits a stronger illusion . This efIn the current study we aimed to investigate how different qualities of touch, involving or not interoceptive processes, determine the strength of the RHI. Specifically, we compared the effects of affective touch (pleasure) to an unpleasant touch (pain), which involves, as well, interoceptive processes while having an opposite hedonic valence (positive vs. negative). Moreover, we compared both these two conditions to a control neutral touch with no involvement of interoceptive processes and expected not to have a specific valence. On one side, it was hypothesized that affective touch and the related activation of the CT system have a unique contribution in modulating bodily awareness, and therefore in determining a stronger RHI. In turn, this may indicate the importance of a positive affective valence of touch in modulating the strength of the illusion. On the other hand, it is also possible that the effect previously found can be generalizable to other forms of interoceptive activation, such as pain (unpleasant touch). If this were the case, results would indicate the importance of an interoceptive involvement in strengthening the illusion, regardless of the related affective valence. Moreover, to disentangle possible mechanisms modulating the strength of the illusion, we assessed the subjective affective experience of the tactile stimulation considering two dimensions: the valence of the stimulation (level of pleasantness and sensation of pain) and the perceived intensity . As a phPreliminary analyses on the subjective evaluations of the visuo-tactile experiences suggest that the three types of touch selected were effective in evoking the expected perceptual sensations: stroking with a brush was reported as the most pleasant condition, while rubbing with a pinwheel was considered the most intense and painful sensation. It is important to notice how the tactile stimulations were successful in target specific subjective experiences, suggesting that pleasantness and painfulness are two separate aspects of subjective perception. Specifically, stroking with a brush was the only stimulation that evoked a sense of pleasantness, while touch with the pinwheel was perceived as a painful sensation even if it was not necessarily unpleasant. Indeed, we carefully chose the painful condition in order to elicit a subjective experience related to pain without causing any harm or discomfort to participants. In line with self-reported levels of intensity, the mean SCL during rubbing with a pinwheel was higher compared to the other tactile conditions, although statistical significance was not reached. Interestingly, the synchrony between the touch felt on the real hand and seen on the rubber hand has an impact on the subjective experience of the tactile sensation. Indeed, in line with previous findings , particiWith regard to the main outcomes of the study, our results are consistent with previous findings based on the RHI. Indeed, both measures of subjective embodiment and proprioceptive drift have been found to be sensitive to the synchronicity of visual-tactile stimulation, indicating that spatial-temporal congruency of multisensory signals is the main driver of the illusion. The different types of touch were found not to significantly influence the strength of the illusion. However, the perceived intensity of the stimulation predicted the mis-location of participants’ hand as closer to the rubber hand (proprioceptive drift). Moreover, participants showed a significant shift away from the initial proprioceptive felt position of their own hand toward the rubber hand during synchronous brush stroking and pinwheel rubbing. Importantly, these two conditions have been reported also to be more intense compared to tapping with a stick and are believed to involve interoceptive processing. Taken together, these results suggest that tactile stimulations associated with interoceptive sensations, such as affective touch and pain, may critically contribute to the proprioceptive localization of the body. Previous evidence suggests that separate mechanisms of multisensory integration underlie the spatial update and feeling of ownership . More spAccordingly, as shown above, participants perceived as more intense also the synchronous condition of the RHI, during which multisensory integration and interoceptive activation are more likely to occur. This is in line with previous research that underlined the link between interoceptive processes and subjective affective experiences. Indeed, interoception has been proposed to be linked to bodily arousal responses and affective/emotional states . NotablyThe multisensory representation of the body is an essential prerequisite for differentiating and comparing oneself with others , which iHowever, the current study presents with some limitations that are worth mentioning. First of all, it should be noted that the sample size was fairly small due to the fact that collection of data was interrupted by COVID pandemic. At the physiological level, arousal was measured in terms of SCL, which typically reflects autonomic responses. However, multi-recording of additional physiological measures would provide a more extensive understanding of physiological mechanisms underpinning the RHI. Specifically, facial electromyography can be used as an implicit measure of affective valence, considering the activation of the zygomaticus muscle (smile expression) that reflects positive affect and activation of the corrugator muscle (frown expression) that reflects negative affect . MoreoveLastly, it should be noted that different individuals may perform differently during the RHI due to differences in personality traits, prior experiences, and attentional mechanisms. For example, musicians have been found to be less susceptible to the RHI possibly due to their prior expertise in motor coordination . SimilarTo conclude, here we showed, that pleasant and painful tactile stimulations, both involving interoceptive processes while having opposite hedonic valence (positive vs. negative), can both induce a drift in the proprioceptive location of the hand during the RHI (proprioceptive drift). Interestingly, these two tactile stimulations were also perceived as more intense by participants. At the same time, intensity of the tactile stimulation was found to predict a stronger proprioceptive drift across the different conditions. We propose that interoceptive information, regardless of the valence of the stimuli (positive and negative) are subjectively perceived as more intense and, through the activation of the limbic system, enhance multisensory integration during the RHI.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Liverpool John Moores University Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study.All authors discussed the project and developed the hypothesis. LD and SS designed the method, prepared the materials, collected and analyzed the data, and prepared the manuscript. All authors reviewed the manuscript and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In quasi-induced exposure (QIE) theory, the presence of hazardous driving action is the typical determinant of the driver’s responsibility for a crash. However, there is a lack of effort available to analyze the impacts of hazardous actions on the QIE estimate, which may result in estimation bias. Thus, the study aims to explore the difference in QIE to crashes involving various hazardous driving actions. Chi-square test is conducted to examine the consistency of non-responsible party distributions among the crashes involving various hazardous actions. Multinomial logit model and nested logit model are employed to identify the disparities of contributing factors to the actions. Results indicate that: 1) the estimated exposures appear to be inconsistent among the crashes with different hazardous actions, 2) driving cohorts have differential propensities of performing various hazardous actions, and 3) factors such as driver-vehicle characteristics, time, area, and environmental condition significantly affect the occurrence of hazardous actions while the directions and magnitude of the effects show great disparities for various actions. It can be concluded that the QIE estimates are significantly different for crashes involving various hazardous actions, which serves to highlight the importance of clarifying the specific hazardous actions for responsibility assignment in QIE theory. Crash exposure is an essential measure in traffic safety analysis that indicates the degree of opportunity for a crash to occur. The exposure family mainly includes direct exposure  and average annual daily traffic (AADT)) and indirect exposure. Quasi-induced exposure (QIE) is one of the indirect exposures that has attracted surging popularity among traffic safety researchers in recent decades. Compared to the traditional direct exposures, the QIE poses a great advantage in indicating the crash opportunity of particular driving cohorts at disaggregated levels such as specific periods and locations. Moreover, the exposure estimation in the QIE framework only relies on a crash database without the need to collect extra data such as traffic volume and traveling distance.Due to the advantages, the QIE method is popularly applied in traffic safety research, particularly in identifying the crash risks of particular driving cohorts such as young drivers, elderly drivers, and commercial vehicles –4. In thIn the application of QIE theory, all crash-involved drivers should be classified into two categories including responsible and non-responsible drivers. Then, the crash exposure can be estimated according to the distribution of the no-responsible driving parties which are assumed to be representative of the overall driving population . There has been a variety of studies –16 devotTypically, there are two kinds of evidence commonly adopted for the responsibility assignment, i.e., the issuance of police citation and the presence of hazardous driving action before the vehicular collision. For the former, a driver would be treated as the responsible party if he/she received a police citation. Here, the citation is determined according to the police officer’s judgment on the traffic violation of the driver. Several previous studies –21 pointAnother responsibility determination method is based on hazardous driving actions performed by drivers. It is more recommended by safety researchers since the QIE is a behavior-oriented technique , 22, 23 In summary, the comprehensive review has revealed that the majority of the existing studies are devoted to validating the “not-at-fault assumption” of QIE theory, in which hazardous driving actions are typically adopted as the determinant to assign crash responsibility to drivers. The responsibility assignment is a key step that can considerably affect the assumption validation and the exposure estimation. However, there are few studies available to discuss which driving actions should be taken into account for responsibility assignment. To fill this gap, the study aims to explore the difference in QIE estimates for crashes with various hazardous actions. The Chi-square test would be employed to examine the consistency of the crash exposures, in which the null hypothesis is that the exposures are consistent for crashes involving different hazardous actions. Then, statistical regression models would be adopted to explore the factors contributing to the occurrence of hazardous driving actions. The findings can serve to clarify the specific hazardous actions for responsibility assignment in QIE theory.QIE technique has been frequently employed to estimate the crash exposure of specific driving cohorts. In the QIE theory, there are two underlying assumptions: 1) the responsibility for a crash needs to be clearly assigned to each driver. Thus, all the crash-involved drivers are classified into two categories, i.e., the responsible drivers (D1) and the non-responsible drivers (D2). Typically, the QIE theory is applied to two-vehicle crashes with a D1 driver and a D2 driver, and 2) the D2s in the crashes are assumed to be the random selection of the overall driving population. Thus, D2 is the target exposure to be estimated.IRi is the crash propensity of specific driving cohort i, D1i and D2i denote the number of D1 and D2 drivers for cohort i, respectively, and ∑iD1i and ∑iD2i denote the total number of D1 and D2 drivers in the two-vehicle crashes, respectively.According to the previous QIE studies , 28, theχ2 can be calculated by:r is the total number of driver-vehicle categories, c is the total number of hazardous action types, fij is the number of D2 for driving cohort i in the scenario of the jth hazardous action, and eij is the expected number of D2 for driving cohort i in the scenario of the jth hazardous action, which is defined as:fi is the ith marginal row frequency, fj is the jth marginal column frequency, and n is the total number of D2s. In the field of statistics, p value <0.05 is typically used to identify the significant difference between comparative data. Particularly, a large number of previous QIE studies adopted this criterion to reject the null hypothesis of the Chi-square test [p value <0.05 as evidence to reject the null hypothesis.The study attempts to employ the Chi-square test to examine the consistency of the exposures to crashes with different hazardous actions. The Chi-square test has been developed and popularly used in previous QIE studies , 16. In are test , 16, 18.Since whether a driver would involve in a specific hazardous action is a discrete choice, two discrete choice models, i.e., the multinomial logit model and nested logit model, are used to reveal the differential factors contributing to the occurrence of various hazardous actions.J different hazardous driving actions that drivers may perform. The individual driver is supposed to perform the action with the highest level of utility. The utility of alternative action j is presented byxj and εj is the error term that includes the impacts of all the unobserved variables, which follows a Gumbel distribution. Then, the probability of choosing alternative j can be calculated byThe multinomial logit model is a typical discrete-choice model that is frequently employed in previous traffic safety research –31. In tM different nests. Then, the probability of choosing hazardous action j can be computed byP(j|l) is the conditional probability of choosing hazardous action j if the nest l is chosen. The second term P(l) is the marginal probability of choosing the nest l.Zj and Wl are the parts of the utility being specific to the hazardous action j and the nest l, respectively; Bl is the set of hazardous actions belonging to the nest l; λl indicates the correlation of hazardous action in the nest l, which is also known as the IV parameter falling between 0 and 1. A higher value of IV means greater independence and less correlation; and It is worth mentioning that the IIA hypothesis of the multinomial logit model does not always hold in fact. Compared to the standard multinomial logit model, the nested logit model is more flexible for relaxing the IIA assumption. It is a generalization of the multinomial logit model that is based on the idea that some alternatives (hazardous actions) can be joined in several specific groups  that share unobserved impacts. The nested logit model has also been developed in previous studies on traffic safety –34. We sL is the value of the likelihood and k is the number of regression parameters.Akaike Information Criterion (AIC) is employed to reflect the goodness of fit of regression models –37.AICThe QIE estimation of particular driving cohorts is based on traffic crash data. The study extracts the crashes from the Michigan crash database (2014) which is provided by the Michigan Department of Transportation. Each traffic crash is investigated and recorded in the crash database in standard procedures. The crash-related variables have been clearly defined and coded in the crash database. According to the statistics, there are nearly 300,000 traffic crashes occurred in Michigan State each year. The large sample size of crashes is sufficient to ensure the stability of the exposure estimate. Moreover, the database contains detailed information on each driving party involved in the crashes, such as the driver’s age, gender, hazardous driving action, and vehicle type. The information on the hazardous action can be used as evidence for crash responsibility assignment in QIE theory. In addition, the crash database also includes extra information such as the time, location, and environmental condition, which can serve to identify the factors influencing the propensity of specific hazardous actions.According to the requirement of QIE theory, it is desirable to conduct data cleaning procedures. The criteria are referred to previous QIE studies , 22, as it extracts two-vehicle crashes with a responsible driver and a non-responsible driver. Here, the responsibility of a driver is determined according to the presence of hazardous action;the crashes involving the missing variables of three key driver-vehicle characteristics  are removed from the database; andthe study focused on two-vehicle crashes, thus, the crashes involving motorcycles, bikes, and animals are not included in the analysis.After the data cleaning procedures, there are 114,666 crash samples finally remained for the data analysis. The Michigan crash database recorded many types of hazardous actions involved in the crashes. The study only focuses on the actions that are frequently observed including speed too fast, failing to yield, disobeying TCD, improper lane use, improper turning, improper backing, and failing to stop in ACD. The actions that are not clearly determined or have few samples are classified into the category of “other actions.” As for the variable selection, the study considers three key driver-vehicle variables  to analyze the exposures of specific driving cohorts. In addition, the road and environmental conditions are also taken into account to identify the contributory factors affecting the occurrence of hazardous driving actions. p values of Chi-square tests are consistently less than 0.05, which verifies the significance of disparity in D2 distribution among the crashes with different hazardous actions.p values are consistently less than 0.05 for driver age, gender, and vehicle type, which are in good agreement with the findings from To further verify the results, Chi-square tests are also conducted for the D2 distribution with the use of Michigan crash data (2012 and 2013). The results , e.g., failing to yield, disobeying TCD, improper lane use, and failing to stop in ACD, while speeding is significantly linked to the roadway with high-speed limit (>50 mph). Actions such as disobeying TCD, improper turning, and failing to stop in ACD are more frequently observed in the intersection area. The urban area is typically associated with the most hazardous action types (except the action of improper backing) compared to the rural area. As for the environmental condition, the dark condition is a factor significantly increasing the probability of disobeying TCD. While other hazardous actions are generally inclined to take place under daylight conditions. Hazardous actions such as speeding and failing to stop in ACD are more likely to be performed when it is rainy & snowy. The finding can be reasonably explained that vehicles required longer stopping distances to avoid collisions on slippery roads , 42. In To validate the reliability of the regression results, several discrete choice models are conducted for the model comparison, including the multinomial probit model, stereotype logit model, and random parameter multinomial logit model. All these models are suitable for the regression with categorical dependent variables, e.g., the different hazardous actions in this study. Note that the random parameter models are the state-of-the-art methods applied in traffic safety research that consider the unobserved heterogeneity of variables –45. In tQuasi-induced exposure (QIE) theory requires the clear-cut assignment of crash responsibility for the crash-involved drivers which is typically determined by hazardous driving actions. The study explores the disparities of QIE to crashes involving different hazardous actions and examines the factors contributing to the occurrence of various hazardous actions. The findings can serve to highlight the importance of clarifying the specific hazardous actions for responsibility assignment in QIE theory.The Chi-square tests demonstrate that the estimated exposures of driver-vehicle cohorts are significantly different for the crashes with various hazardous actions. Cohorts such as male and mid-age drivers, pickups, and heavy vehicles have relatively larger exposure to crashes involving speeding than those involving other hazardous actions. A feasible interpretation is that the specific driving cohorts are expected to occur in the locations where the corresponding hazardous actions are frequently performed. For instance, heavy vehicles usually travel on specific routes such as freeways where speeding behavior is prone to occur while other hazardous actions have fewer opportunities to be performed , 47. ThiThe results of the IR calculation indicate that the particular driving cohorts have different propensities for performing various hazardous actions. As anticipated, young drivers have relatively higher risks of involving all kinds of hazardous actions  while mid-age drivers indicate the opposite, which is in line with previous QIE studies , 50–52. The regressions of the multinomial logit and nested logit models indicate that the influential factors show great disparities for the various hazardous actions. Particularly for driver-vehicle characteristics of the responsible parties, it is observed that the direction and the magnitude of the impacts are not consistent across the various scenarios, which can somewhat serve to warrant the presence of disparities in the propensity of involving various actions for specific cohorts. The vehicle types of the innocent parties can also be significant contributors to crashes with specific hazardous actions. Generally, passenger cars & vans are more likely to be collided as opposed to pickups and heavy vehicles. An opposing finding is that speeding vehicles are prone to collide the pickups and heavy vehicles. The displayed information also indicates that the occurrences of hazardous actions are affected by a number of factors regarding time, location, and environmental conditions. Actions such as failing to yield, disobeying TCD, and improper turning are usually performed in the intersection area; thus, the intersection area is identified to be the contributory factor to these driving actions in the regression results. In terms of the light condition, all hazardous actions are inclined to occur under daylight except for the action of disobeying TCD. The finding is somewhat inconsistent with a previous QIE study on red light running crashes that the crash risk is higher in the daytime . A possiThe estimated parameters are similar in the statistical models, which serves to further demonstrate the validity of the findings. Notwithstanding, it is found that the nested logit model has a relatively lower AIC value than the multinomial logit model, which suggests that the nested logit model has better goodness of fit. This may be attributable to the different correlations among the various hazardous actions. In the nested logit model, the hazardous actions in the same nests generally have stronger correlations than those in the different nests. For example, both the actions of failing to yield and disobeying TCD are closely related to the right of way and are prone to occur in intersection areas. Nonetheless, considering that there are a variety of hazardous action types, further efforts can be directed to explore the different classifications of hazardous actions for the nests to achieve better goodness of fit. To further understand the characteristics of hazardous actions, it also calls for developing advanced analytic methods to identify the heterogeneity  and spatHazardous driving actions are the typical determinant for crash responsibility assignment in QIE theory. With the use of Michigan crash data, the study explores the difference in QIE for crashes with various hazardous actions. The analytic results demonstrate that 1) the exposures are inconsistent among the crashes involving different hazardous actions, 2) driving cohorts have differential propensities for performing the various hazardous driving action, and 3) the occurrences of hazardous actions are influenced by many factors such as driver-vehicle characteristics, time, area, and environmental condition. The findings serve to highlight the importance of clarifying the specific hazardous actions for responsibility assignment so as to improve the accuracy of QIE estimation.The limitation of the study is that it only compares the exposures between crashes with different hazardous actions, while does not assess the accuracy of exposure estimates by comparison with the exposure truth  due to the lack of relevant data. Future work can be directed to further validate the accuracy of the exposure estimation under specific responsibility assignment criteria when the data of exposure truth is available.S1 Data(CSV)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.S1 File(PDF)Click here for additional data file.


Wearable devices and smartphones that are used to monitor the activity and the state of the driver collect a lot of sensitive data such as audio, video, location and even health data. The analysis and processing of such data require observing the strict legal requirements for personal data security and privacy. The federated learning (FL) computation paradigm has been proposed as a privacy-preserving computational model that allows securing the privacy of the data owner. However, it still has no formal proof of privacy guarantees, and recent research showed that the attacks targeted both the model integrity and privacy of the data owners could be performed at all stages of the FL process. This paper focuses on the analysis of the privacy-preserving techniques adopted for FL and presents a comparative review and analysis of their implementations in the open-source FL frameworks. The authors evaluated their impact on the overall training process in terms of global model accuracy, training time and network traffic generated during the training process in order to assess their applicability to driver’s state and behaviour monitoring. As the usage scenario, the authors considered the case of the driver’s activity monitoring using the data from smartphone sensors. The experiments showed that the current implementation of the privacy-preserving techniques in open-source FL frameworks limits the practical application of FL to cross-silo settings. To detect potentially dangerous driver activities or states such as distraction or drowsiness, modern driver monitoring systems make use of different data sources such as in-vehicle sensors, smartphones and wearable devices. Such devices collect a lot of sensitive data such as video, audio, location etc. The processing and analysis of these data are regulated by special legislative regulations such as GDPR  and PDPAIn this paper, the authors focus on the case of commercial vehicle activity monitoring based on the data from smartphone sensors. These data include readings from gyroscopes, accelerometers, GPS and other smartphone sensors, and describe the driver’s behaviour via the vehicle state. The analysis of such data contributes not only to the traffic safety and driver’s security but also to optimizing business processes as they allow enhancing simulation models for technological, construction and/or logistic processes . ConsideAnother attractive feature of the FL paradigm is a decrease in network traffic because model parameters only are transferred, and they are usually significantly smaller than the initial training data. Thus, FL proposes an attractive computational model for distributed machine learning that could be used when designing information systems such as driver monitoring systems that require processing sensitive and personal data.However, despite these observable privacy improvements in distributed machine learning, there are no formal proofs of the privacy level guaranteed by federated learning settings . Recent TensorFlow Federated (TFF) ;Federated AI Technology Enabler (FATE) ;Paddle Federated Learning (PFL) ;Federated Learning and Differential Privacy (FL & DP) ;FEDn .In this paper, the authors analyse the privacy mechanisms developed for FL systems and investigate what privacy-preserving technologies are implemented in the open-source FL frameworks such as:The focus of this paper is privacy-preserving mechanisms implemented in these frameworks and their impact on the overall training process, i.e., global model accuracy, training time and network traffic generated during the training process for the driver’s or vehicle state recognition task. These parameters allow assessing the applicability of FL frameworks to different FL settings that are different in resource requirements, client’s behaviour and communication topology. Though there are a number of surveys devoted to different aspects of federated learning including privacy issues, they do not assess the application of privacy-preserving techniques to practical analysis tasks. For example, Kairouz et al.  presenteanalysis and systematization of the privacy-preserving mechanisms developed for the FL systems;analysis of the privacy-preserving mechanisms available to the users in the open-source FL frameworks;assessment of their impact on overall training performance of FL systems.Thus, the main contributions of this paper are as follows:The novelty of the paper consists in that all privacy mechanisms are reviewed in terms of the properties of FL systems such as computational topology, data partition, requirements to the computational resources and ability to treat drop-outs of the clients. This allowed the authors to identify an existing challenge in adopting a privacy mechanism in de-centralized cross-device settings where clients are represented by the IoT devices. The analysis results of the privacy-preserving mechanisms implemented in the open-source federated learning frameworks will be helpful when choosing a privacy mechanism as well as the framework that implements it, considering their limitations and capabilities.The paper is structured as follows. The privacy in driver’s behaviour and vehicle state recognition systems based on smart device sensing includes the privacy of the information that could be extracted from sensors’ readings and the privacy of the data that describes how the driver interacts with the system and its sensing components ,18. E. MThe driver’s behaviour recognition and monitoring systems require analysis of a wide range of personal data, from location data to health data. The threat model for the federated learning includes such attacker characteristics as type, their capabilities, goals and knowledge . The attThe attacks could be performed during the training phase or the inference phase.Evasion attacks are implemented during the inference phase, and they assume crafting adversarial samples that target to deceive the system. This is the most common attack on machine learning-based systems.The target of the attacks in the training phase is the FL model itself, and depending on the attack goal, the attacker may try to learn, influence or corrupt it. It is possible to outline two generic types of attacks implemented during the training phase, they are inference attacks and poisoning attacks. The attacker can run data or model poisoning attacks to create a backdoor for future attacks by corrupting the integrity of training data set collection or the integrity of the learning process.class representative attacks that allow an adversary to recreate samples like the samples of the original training dataset;membership inference attacks that target to determine if a given sample was used in the training process;property inference attacks that target to learn patterns and important properties of the training dataset such as class balance in the dataset;sample/label inference attacks that allow an adversary to obtain not only training samples but also labels with it;source membership attack .Inference attacks target an individual participant’s update or an aggregate of updates from all participants, and their primary goal is the privacy of data owners participating in the FL process. The application of the machine learning techniques often involves analysis of sensitive data such as health data and financial information and it has been shown that trained analysis models may be used to reveal information about the used training dataset, and therefore, impact the data owner’s privacy ,20,21. Bdifferential privacy (DP) mechanisms;data and model encryption techniques that include multi-party secure computations (MPC) and homomorphic encryption (HE);trusted execution environment (TEE).Xu et al. suggest differentiating between the privacy of the input and privacy of computation in federated learning settings . The pria semi-honest adversary that could corrupt parties but follows the protocol as specified, such adversaries are also referred to as honest-but-curious);a malicious adversary that can arbitrarily deviate from the protocol specification, such adversaries are often called active ones.The authors also considered a security model that is supported by different privacy-preserving approaches. The security model includes a type of adversary that is used to evaluate the protocol and number of clients that could be corrupted. There are two types of adversaries:The ability of the protocol to operate in malicious adversary settings provides stricter security guarantees than in the semi-honest adversary model. For some protocols, the number of clients that could be corrupted is an essential security parameter. If such a number is greater than a half of collaborating clients then such settings are referred to as malicious majority, the rest of the cases are referred to as honest majority.Differential privacy (DP) is a formal privacy-preserving model with mathematical proof . The ideDifferentially private algorithms are characterized by two parameters: The application of the DP mechanisms to secure data privacy requires the adoption of a particular machine learning algorithm. Gong et al.  showed tKariouz et al.  outline i et al.  suggeste leakage .The distributed DP model is aimed to solve these problems by introducing encoding and/or shuffling techniques that add additional privacy guarantees to locally differentially private inputs of the clients. Most of them ,32,33 arFor completeness, it is necessary to remark that differentially private algorithms are elaborated primarily for the horizontal data partition. This conclusion follows directly from the definition of differential privacy that operates with datasets differing in the record but not in the set of attributes. Currently, there is not much research devoted to the differentially private algorithms for vertically partitioned data. Existing solutions use either a semi-trusted party that assists in generating synthetic integrated databases, like in  or combimulti-party computation (MPC);homomorphic encryption (HE);functional encryption.Secure computations is a set of approaches and technologies that aim to provide opportunities to preserve the privacy of input data when computing some function jointly. Currently, there are three different cryptography-based approaches to secure sensitive data processing:F depending on their inputs without revealing their secret inputs. The key idea of HE is to perform computations on encrypted data. Functional encryption is a public-key cryptosystem that allows parties to encrypt their data, meanwhile, an external entity can compute a specific function on the ciphertext without learning anything additional from the underlying plaintext data [All these three approaches have different properties and usage scenarios, but all of them are applicable to the federated machine learning paradigm. For example, in MPC, two or more parties jointly compute some function ext data . The latext data .Application of the MPC protocols in FL settings assumes usage of the server-aided scheme with several servers. The clients who own data provide their data (encrypted or masked) to a set of servers to perform computations. Current implementations of the MPC protocols that could be used in real-world applications are limited in the number of collaborating computational servers—two , three  or four The key distinctive feature of the HE is that it allows the third party  to apply certain computable functions on the encrypted data while preserving the features of the function and format of the encrypted data.In the FL setting, the application of HE limits the amount of information that other participants or aggregating servers can infer from observing the training process and model parameters.Similarly to the MPC mechanisms, HE algorithms need to be adapted to machine learning models, aggregating function ,43,44 anAnother critical issue that needs to be considered when applying the HE protocols in FL settings is that almost all well-known schemes ,47 allowBoth MPC and HE protocols are characterized by significant computational and communicational costs, because encryption and/or decryption involve multiple modular multiplications and exponentiation operations with a large exponent and modulus , making them extremely expensive to compute. Thus, the main challenge in the application of MPC and HE to secure the privacy of FL is in accelerating computation and reducing communication overhead ,51,52. FNowadays, HE is often used as a building block of MPC protocols. Mixing MPC with additive homomorphic encryption allows constructing secure and computational efficient solutions . Usage oramework  for threramework  frameworramework  for the Thus, in general, existing encryption-based solutions, due to their performance characteristics, are practically inapplicable in cross-device settings and are typically used in cross-silo settings. They also could be used in cases when computations could be delegated to a small set of trusted computational servers that do not collude .Secure aggregation (SecAgg)  is a ligAnother factor that impacts the protocol performance is the necessity to handle clients dropouts and the potential malicious behaviour of the aggregation server. The latter problem could be solved by applying public-key cryptography. To optimize the communicational costs associated with the usage of cryptographic protocols, some recently suggested approaches use the idea of replacing complete communication graph with random subgroups of clients and application of secret sharing only for a subset of clients rather than all pairs of clients ,59,60,61In , the autConfidentiality that guarantees that the current state of the code execution remains confidential unless it receives the corresponding verified notification;Integrity that guarantees that code alteration, as well as its execution path, could not be changed unless it receives a corresponding verified notification;Attestation that allows TEE to prove that code is currently executed and to demonstrate its initial state.As defined in , a trustN. Bouacida and P. Mohapatra  broaden Another benefit of TEE is that the code execution in a secure enclave of the untrusted device is done almost at native speed when compared to other privacy-preserving mechanisms.In  the authHowever, the current TEE environment is available only to CPU devices and is limited in memory, thus making it impossible to take the benefits of training deep learning models on GPUs or machine learning processors. Existing research also has shown that TEEs are vulnerable to different types of attacks—attacks exploiting flows in the architectural solution of a particular secure enclave, side-channel attacks or attacks utilizing software flows such as buffer overflow and bad usage of cryptographic protocols ,68. KariA number of FL frameworks have recently appeared. In this paper, the authors review privacy-preserving mechanisms implemented by five open-source FL frameworks that are currently under active development and demonstrate a relatively high technology readiness level. They are TFF , FATE 1, PFL 1111, FL & TensorFlow Federated (TFF 0.17.0) adopts two types of privacy-preserving mechanisms. They are a secure aggregation strategy as it is presented in  and DP-SFATE 1.5.0 focuses on the adoption of a variety of encryption-based techniques that are used to secure training of vertically and horizontally partitioned data. They implement a variety of homomorphic encryption schemes including Paillier, RSA-based and others . The DifPFL 1.1.0 supports both differentially private- and encryption-based techniques designed for training NN on horizontally partitioned data and makes use of multi-party secure computation protocol (ABYprotocol ) to perfFL & DP 0.1.0 puts forth differentially private mechanisms to preserve the privacy of input data and implements a variety of adaptive mechanisms based on exponential, Laplacian and Gaussian noise and sensitivity based sampling and subsampling.FEDn 0.2.0 leverages TEEs to ensure the veracity of the model updates done by clients. The technical challenge is to do this in a way that does not add unacceptable overhead in computation, since the efficiency of the model updates is also essential to federated learning.This section reviews privacy mechanisms implemented by open-source FL frameworks. What privacy-preserving mechanisms are implemented, and what protocols are used?What analysis models are they intended for?Are there any specific limitations on their practical application?How do privacy mechanisms impact the training process in terms of accuracy, time and network traffic?When assessing the privacy-preserving aspects of these systems the authors investigated the following aspects:As a use case, the authors considered the task of commercial vehicle monitoring. The authors studied the task of the dumpers’ activity analysis and tracking, the results of such analysis could significantly contribute to the productivity as well as the sustainability of the construction industry. Dumpers are the machines that are used to transport material such as soil, sand and rocks from the excavation point to the storage site in an earth-moving cycle. Smartphone sensors such as accelerometer and gyroscope in conjunction with the GPS could be used to monitor four main activities of the dumpers: loading the material, transporting it to the destination point, discharging it and driving empty to the excavation point to get loaded again. In this use case, the collaborating entities could be represented by the companies managing construction sites who do not want to share such information for commercial reasons and/or vehicle manufacturing companies who are interested in providing driver and/or vehicle activity monitoring systems as an additional service.The authors used a signal data set that also describes the movement of two vehicles—dumpers of the AH and HL series. These dumpers operated at a ground remediation site in Sweden, and the collected attributes include the timestamp, speed, gyroscope and accelerometer data ,72. The single hardware hyper-thread in 2.0–3.8 GHz core,RAM 6.5 GB (RW speed 24 Mb/s),swap: 180 GB, HDD: 30 GB (RW speed 24 Mb/s).The following experimental settings were used. The Google Cloud virtual machines were used as a runtime environment. Each virtual machine had the following characteristics:In the experiments, configurations with two and four virtual machines were used in order to evaluate the impact of privacy mechanisms depending on the number of clients.To model horizontal data partition, the original data set was divided between clients into equal shares: each client stored ¼ of the original data set in the experiments with 2 and 4 clients, thus each data set contained approx. 420,000 samples with 7 attributes and 1 label.four clients: the first client had three attributes, the second and third—two attributes each, and the fourth client had one attribute and one label;two clients: the first client had six attributes, and the second client—one attribute and one label.To model vertical data partition, the authors split the data across the clients in the following way:The authors also reduced the size of the data set to 10,000 samples as the duration of the experiments lasted more than 4 days. The previous set of experiments  showed tFATE 1.5.0 provides a quite wide variety of privacy-preserving algorithms for data analysis, they include algorithms for calculating statistics for vertically distributed data, feature selection and feature encoding. However, in this paper, the authors focus on privacy-preserving algorithms used to train models in federated mode.Currently, FATE 1.5.0 supports the training of neural networks of different types, linear regression models and gradient boosting decision trees. Moreover, these models could be trained on both horizontally and vertically partitioned data.To protect data privacy, FATE utilizes encryption-based techniques only—secure aggregation and different homomorphic encryption schemes. For horizontally partitioned data, FATE supports a secure aggregation strategy that uses encryption of the client’s updates and additive homomorphic encryption schemes for vertically partitioned data. This privacy mechanism is based on the algorithms proposed by the authors of , and is The privacy is achieved by adding specially constructed random numbers generated on the basis of clients’ secret keys, it uses the Diffie–Hellman key exchange protocol that is implemented implicitly for the FATE end user.To assess the impact of the privacy mechanisms on the training process, the authors used FedAvg aggregation strategy  with simThus, the use of this privacy mechanism almost does not impact the training process. Moreover, it does not have any limitations on the number of clients participating in federated learning, it could be considered the right choice for training neural networks or decision trees.In FATE, homomorphic encryption is used to secure the training of neural networks, decision trees and linear models for vertically partitioned data. Despite the certain differences in implementations, all the encryption-based aggregation strategies share one idea: all linear computational operations such as summing are secured by additive homomorphic encryption, while all nonlinear computations are done locally in plain text. For example, this idea is implemented in the SecureNN algorithm  that is The authors suggest considering the bottom (unlinear) model as the “feature extraction” subtask, the interactive layer that is secured by additive HE and implements the role of the simplified classifier, and the top model fine-tunes the results of the simplified classifier. This computational model defines certain requirements for the clients (participants of computations). One client holds data and labels, and this client trains the top model  algorithm is quite similar to the SecureNN model. There is a party that holds data and labels (referred to as active or guest) and implements secure aggregations, and parties that hold only data (passive users or host) . HoweverTo save communication and encryption costs, the Fast SecureBoost algorithm is suggested and the modifications of the algorithm relate to different strategies while building decision trees. For example, in MIX mode Fast SecureBoost limits the number of trees constructed per party, layered mode supports only one guest party and one host party. Developers of FATE claim that the application of Fast SecureBoost results in a 30–50% reduction in training time. In our experiments, the authors used Fast SecureBoost and evaluated the impacts of different encryption schemes—Paillier and iterative affine HE schemes—on the accuracy, training time and network traffic. FATE 1.5.0 suggests several linear classification models implemented in the federated model for vertically partitioned data: linear regression, logistic regression and Poisson regression. These models are also secured by partially homomorphic encryption. However, the computational model slightly differs from one used in secure training of NN and SGBDT. It assumes three different roles assigned to clients—arbiter (aggregator), guest and host. Arbiter is a node (server) that orchestrates not only training processes but also key management procedures, it delivers keys to guest and host, and the guest holds data and labels, while hosts have only data. The computed gradients are locally encrypted by hosts and guests and transmitted to the arbiter, who aggregates, calculates and transfers back the final gradients to corresponding parties. To encrypt gradients, the Paillier, iterative additive and random iterative HE are used. The default encryption scheme is Paillier with a 1024 key size.Experiments showed that, similarly to training other models secured with HE schemes, training regression models regardless of their type  is a time-consuming task. The authors evaluated training of the linear regression for two and four clients. In both cases clients held data sets of similar size, the training was performed with a batch size set to 1000. Currently, PFL implements a centralized (server-aided) model for aggregating parameters of models, and almost all privacy-preserving algorithms implement a similar computational model. As currently, PFL focuses on training neural networks of different types in federated mode  the privacy mechanisms are targeted to protect neural network parameters and use the peculiarities of neural network (NN) training.For horizontally partitioned data, PFL supportsDP-SGD algorithm for differentially private NN training;secure aggregation, encryption-based algorithm for NN training.For vertically partitioned data, PFL implements a pair of connected protocols ABY3  and PSI 3 , where AIn fact, ABYTo assess the impact of the privacy mechanisms on the training process, the authors used the FedAvg aggregation strategy  with simExperiments showed that in general case secure aggregation (SecAgg) strategy shows results that are comparable with results obtained when training with FedAvg strategy. As in the case of FedAvg, the PFL showed unstable behaviour when training NN with batch size equal to 32 . The expWhen the number of rounds is set to 20, traffic generated by SecAgg is almost the same as the traffic generated by FedAvg, and is slightly higher for the case of 4 clients and batch size set to 32. Thus, it is possible to conclude that the encryption of the model weights does not impact the overall network load. The increase of the network traffic in case of batch size set to 32 could be explained by the increase in communication between server and client to monitor the training process. When the number of rounds is set to 10, the traffic for SecAgg significantly increases. Moreover, for the case of two clients, it is possible to see that this growth depends on the size of the batch: it grows as the batch size decreases. For the case of four clients, there is no observable dependency. In general, the training time for both strategies is very similar, in both cases, it depends on batch size and does not significantly depend on the number of clients and the number of rounds. However, the authors observed two unusual bursts in time for batch size 100 and the number of clients equal to 2. Correlating these data with the network data  allows cPFL supports the DP-SGD strategy for training NNs and regression models. The federated implementation of DP-SGD in PFL implements the local privacy model, i.e., the data privacy protection by adding noise is done locally on the client. However, the setting of the privacy budget is done in a centralized manner, and this parameter is the same for all clients.It is possible to set up the privacy budget The accuracy of DP-SGD turned out to be lower than the accuracy of the analysis model trained using FedAvg . When trUnexpectedly, the training time of DP-SGD grows drastically with an increase in the number of clients and the batch size. When the authors assessed the network traffic generated by DP-SGD, it was discovered that its volume significantly exceeds traffic generated by SecAgg and FedAvg: if the traffic volume for FedAvg and SecAgg is measured in MB , the tra(1)Definition of intersection of data sets belonging to different data owners (clients). The implementation of this step is necessary when data sets belonging to different clients do not have similar sets of records’ IDs. The clients’ data set alignment is implemented using the private set intersection (PSI) protocol, which is based on the Naor–Pinkas oblivious transfer protocol described in . This pr(2)Secret Sharing step. Afterwards, the clients determined the common part of their datasets, they initiate a secret sharing procedure, when they share their dataset among computational nodes. Usage of the MPC protocol as it is described above requires having two or three computational nodes that perform computations over masked data. In PFL ABY(3)Private model training process. The training of the analysis model on shared and masked data is performed by three computational nodes using the ABY(4)Result reconstruction. After the result of a secure computation has been obtained, it is necessary to reconstruct the results, as the initial input data were split among three computational nodes. The reconstruction of the results in PFL is done based on previously used arithmetic sharing but in reverse order aggregating the shares. PFL allows setting the entity responsible for reconstructing results, this entity is called Result Party, and this role could be assigned either to the Computing party, data owner (client) or any other trusted third party.PFL implements the processing of vertically partitioned data with help of MPC protocols. The generic scheme of the analysis of the vertically partitioned data is shown in When assessing the efficiency of MPC-based training, the authors omitted the first step (finding PSI) and the second step (making encrypted shares), only the time of the third step was assessed. Currently, in PFL, not all MPC operators for setting up neural networks are implemented. For example, PFL has batch_normalization and dropout layers, which are not implemented in MPC. That is why the test neural network was feed-forward with one input layer, two hidden layers (256 and 128 neurons), an activation layer and an output layer. Apart from this, some function activation operators and operators for calculating metrics are not implemented yet. That is why the accuracy is calculated manually as a ratio of correct predictions to all samples.When batch size is set to large numbers the neural network does not train properly producing always the same result with accuracy ∼50%. The training time of NN using MPC depends a lot on the initial size of the dataset  and the size of the batch. Thus, for example, for a training network on a dataset consisting of 10,000 records with a batch size of 1000, the training time equals 16 min, while a training network with similar parameters on the same data set but of a larger size  results in more than 27 h. The experiments showed that PFL and FATE implement almost similar privacy mechanisms for protecting horizontally distributed data, PFL also supports a differentially private strategy—DP-SGD—to train the NN. To protect vertically distributed data, PFL uses MPC based strategies, while FATE focuses on the application of partially homomorphic encryption. The latter has allowed FATE to support more analysis models for training them in a secured federated mode.The secure aggregation strategy is quite similar to the FedAvg strategy in terms of accuracy, training time and network load. In PFL the authors observed bursts in traffic that are most likely explained by peculiarities of PFL internal implementation common to all strategies. The observed burst time most likely depends on network performance during experiments, and this fact has to be considered when assessing the applicability of PFL in practice. Thus, it is possible to conclude that the implementation of secure aggregation in FATE is more stable and almost similar to FedAvg.The current implementation of DP-SGD in PFL looks rather raw, it suffers from unexpectedly high traffic as well as long training time, especially when considering that no resource consuming operations are implemented. However, it is necessary to note that accuracy grows with the number of clients using these privacy mechanisms when the federated learning environment assumes a quite large number of users.All FATE privacy-preserving mechanisms based on partially homomorphic encryption are very time and memory-consuming and are characterized by a high volume of traffic. Searching for appropriate model parameters may result in a very long process.Another interesting finding is that all implemented privacy-preserving techniques support a centralized communication scheme that supposes the usage of a server-aggregator. This server-aggregator has access to data or model weights. This means that parties except party-aggregators have no possibility to make inferences about data belonging to other clients, however, the party-aggregator has such possibility, and in case of his/her compromise, the system becomes vulnerable to inference attacks. This establishes high requirements for the security and trust level of the aggregating server.The authors’ previous research  has showModern driver monitoring systems utilize data from different types of sensors such as in-vehicle sensors, wearable devices or smartphone sensors. These data often contain personal or other sensitive data that requires additional privacy and security protection. Federated learning is a possible practical solution that allows finding the trade-off between the privacy of the data owner and the efficiency of machine learning algorithms. It could be used to design a driver or vehicle monitoring system that enables privacy-preserving collaboration of the data owners. However, it has been shown that federated learning still has vulnerabilities that impact input data privacy.This paper reviews and systematizes approaches proposed to enhance the privacy of the FL process. Their analysis is done with respect to the key features of FL systems such as communication topology, data partition, requirements to the computational resources and ability to treat drop-outs of the clients.To evaluate the implemented privacy-preserving techniques in the open-source FL frameworks, the authors used the case of commercial vehicle monitoring. The experiments showed that while there are a number of research papers offering solutions that could deal with client drop-out, the privacy-preserving techniques available in the open-source FL platforms do not tolerate client drop-outs and have high requirements of computational and memory resources and network bandwidth. This allowed the authors to conclude that the current implementation of privacy-preserving techniques is suitable for the cross-silo setting only. In the context of the driver or vehicle activity monitoring task, this means that the practical application of the existing open-source FL frameworks with privacy-preserving techniques is limited to the cases when collaborating clients have enough computational resources. For example, it could be applied when designing commercial vehicle activity monitoring system, when the role of the aggregating server is implemented by trustworthy entity.The detailed comparative analysis of the two open-source frameworks FATE and PFL in terms of privacy-preserving techniques showed that they implement slightly different approaches to enhance the privacy of the data owners, for example, FATE uses homomorphic encryption to secure computations and input data, while PFL uses MPC protocols to protect the processing of vertically partitioned data and differential private strategy to train neural networks for horizontally partitioned data. However, both of these frameworks support a secure aggregation strategy whose computational performance is comparable to the aggregation strategies without additional privacy mechanisms. In general, FATE shows a much higher technological readiness level and could be used to develop privacy-preserving FL–based driver or vehicle monitoring systems despite the high memory requirements and long training process. The recommended privacy-preserving strategy for this task is secure aggregation as it demonstrates similar results in accuracy and temporal performance as an aggregation strategy without privacy protection.To conclude, it is necessary to note that the implemented privacy mechanisms are not secure against poisoning attacks. Malicious clients regardless of their roles in the FL system could manipulate their inputs or aggregated weights in order to bias the global model. The future direction of the research includes the assessment of the impact of the poisoning attacks on driver and vehicle activity recognition systems based on federated learning.


Previous works have shown that risk factors are associated with an increased likelihood of colorectal cancer.The purpose of this study was to detect these associations in the region of Lleida  by using multiple correspondence analysis (MCA) and k-means.This cross-sectional study was made up of 1083 colorectal cancer episodes between 2012 and 2015, extracted from the population-based cancer registry for the province of Lleida (Spain), the Primary Care Centers database, and the Catalan Health Service Register. The data set included risk factors such as smoking and BMI as well as sociodemographic information and tumor details. The relations between the risk factors and patient characteristics were identified using MCA and k-means.The combination of these techniques helps to detect clusters of patients with similar risk factors. Risk of death is associated with being elderly and obesity or being overweight. Stage III cancer is associated with people aged ≥65 years and rural/semiurban populations, while younger people were associated with stage 0.MCA and k-means were significantly useful for detecting associations between risk factors and patient characteristics. These techniques have proven to be effective tools for analyzing the incidence of some factors in colorectal cancer. The outcomes obtained help corroborate suspected trends and stimulate the use of these techniques for finding the association of risk factors with the incidence of other cancers. Colorectal cancer is the third most common type of cancer worldwide ,2. In EuIn the province of Lleida (Spain), the population-based cancer registries allow the identification and counting of all incident cases (new cases) diagnosed among the residents of this geographical area . The resSome studies have demonstrated a higher incidence of colorectal cancer among those with low SES and risk factors such as BMI and smoking. A pooled European cohort study  demonstrRecent research has applied the techniques used in this study, but none of these studies were for cancer and risk factors. Ugurlu and Cicek  used theSeveral studies -10 have As the main contribution of this study, we propose the use of MCA as a statistical technique to detect relations between risk factors and patients’ characteristics and k-means as an unsupervised learning algorithm to search for clusters of patients with similar risk factor profiles for colorectal cancer.The main information sources were the population-based cancer registry of the health region of the province of Lleida, the eCAP  softwarThe colorectal cancer data were extracted from the new cases registered between 2012 and 2015 in the Lleida population-based cancer registry ,18 for pThe BMI was used to calculate the obesity of each patient by standard weight status categories . We cateMCA is an unsupervised learning algorithm for visualizing the patterns in large and multidimensional categorical data . This meThe factors were interpreted with the help of various statistical coefficients, which complemented each other to provide a better interpretation. The most common and important are inertia, eigenvalue, contribution, and factorial coordinates. Inertia is a measurement of the dispersion of the set of computed distances between points. Analogously, in principal CA, inertia corresponds to the explained variance of dimensions. The eigenvalue allows the inertia that a specific category produces to be quantified determining a certain percentage relative to the entire set of the active category. The percentage coordinates (x- and y-axis) of the graph enable the category points in a graph to be represented and established. In MCA, the distance between 2 or more categories of different variables can be interpreted in terms of the associations and correlations between these. If 2 categories present high coordinates and are close in space, this means that they tend to be directly associated ,27. If 2k predefined distinct nonoverlapping subgroups (clusters) where each data point belongs to only one group. It tries to make the intracluster data points as similar as possible while also keeping the clusters as different (far) as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster’s centroid is at the minimum. The less variation we have within clusters, the more homogeneity (similarity) there is between the data points within the same cluster. The k-means algorithm is composed of the following steps: (1) it places k points in the space represented by the patients who are being clustered, (2) it assigns each patient to the group that has the closest centroid, and (3) when all patients have been assigned, it recalculates the positions of the k centroids. Steps 2 and 3 are repeated until the centroids no longer move. This produces a separation of the patients into homogenous groups while maximizing heterogeneity across groups. The optimal number of clusters was obtained by the elbow method [K-means  is a nonw method . This cow method .All the information presented was analyzed using MCA, an extension of CA, and the k-means algorithm. The combination of MCA and k-means benefits the effectiveness of the calculation process and, in consequence, the k-means results. MCA helps to reduce the noise, which allows the k-means algorithm to obtain more accurate distances. The MCA dimension reduction automatically performs data clustering according to the k-means objective function . In addiThe MCA method was implemented in scripts performed with R , an openThe analysis of the MCA and k-means without the stage variable included 1083 registers. Graphically, the points closer to each other or the points represented in the same direction of the axis suggest associations. As can be seen, mortality and older age are very close in the plot. This suggests a possible association. Another possible relation observed could be between females and obesity. Then, a cloud on the positive x-axis and the negative y-axis was made up of the 65-74 years age group, high income, and survival. Finally, additional associations could be made up of the 50-64 years age group, males, smokers or ex-smokers, and normal weight.This subsection presents the outcomes, including the stage of the tumor. The data set used for this analysis discarded the registers, which did not contain the stage (647 registers). Therefore, the number of cases analyzed was 438 . Figure The MCA technique and the k-means algorithm permit the analysis and detection of clusters of patients with similar risk factors and outcomes not observed in the literature. The population-based cancer registry for the province of Lleida registered 1083 colorectal cancers between 2012 and 2015. This cancer is the most incident in our region ,18 and bPrevious studies have used clustering techniques to detect associations, but none of them were used for associating patient profiles with risk factors. We based our study on a preliminary paper , which eOne MCA outcome was the inertia (27%). Further, various variables had high contributions. A strong relation was obtained between older patients (≥75 years age group) and mortality. This may suggest an increase in the risk of mortality for colorectal cancer in older adults, as previous studies showed . On the Regarding the k-means analysis, the third cluster confirmed the mortality in the older population with obesity . The firThe analysis then studied the data set filtered by tumor stage. The final data set was made up of 438 registers. The MCA technique obtained a significant relation between stage III and mortality. However, screening programs and technology decrease this risk, as recent studies concluded . We can The strengths of using the MCA and k-means cluster analysis are that the results are less susceptible to outliers in the data, the influence of chosen distance measures, or the inclusion of inappropriate or irrelevant variables . This stIn conclusion, many studies demonstrate that some risk factors such as BMI, tobacco smoking, or SES could influence the incidence of colorectal cancer by using traditional techniques. This study used new techniques such as MCA and k-means to analyze the relationships between colorectal cancer and risk factors. The outcomes obtained demonstrated that the combination of these techniques could help to detect relations between risk factors and patient characteristics. Obesity and being overweight in the older population (≥75 years age group) increases the risk of developing aggressive tumors and death. Stage 0 was related with younger people and survival. This highlights the importance of screening programs for colorectal cancer. The presence of tobacco in a cluster indicated that it must be considered as a risk factor in colorectal cancer. The results of our study help to corroborate suspected trends in several of the relationships detected and confirm the usefulness of these techniques. Further, they encourage applying these methods to other cancers and detecting how the risk factors could be associated. In future work, it is important to delve deeper into the patients’ characteristics and risk factors. This means including new variables such as diabetes, alcoholism, or the cause of death. The findings obtained in this study motivate us to search for relations between risk factors in other cancers. Moreover, new techniques and artificial intelligence algorithms can be implemented to explore patterns of pretumor and posttumor detection from the clinical history.


Studies have found that green practices can help organization create unique competitive advantages, such as enhancing the brand value. However, in the existing research, people did not know much about the exhibition audiences’ perceptions of green practices, or its impact on brand loyalty. This study explores the dimension of green practice perceptions of exhibition audiences, uses the trust-commitment theory to verify the relationship between green practice perceptions and exhibition brand loyalty. A total of 665 valid questionnaires were collected in the two exhibitions. The results show that four of the five types of green practices perceived by the audience will significantly affect green trust and then brand loyalty through commitment. Two of the five types of green practice perception can directly affect brand loyalty. And green self-identity will significantly adjust the relationship between green trust and affective commitment. Finally, suggestions are proposed for academic and practical reference. As people continue to pay attention to environmental issues, the environmental protection features of products or services are increasingly being valued by consumers. The green practices of enterprises are not only conducive to environmental protection, but also bring positive economic and social benefits to enterprises. Studies have found that companies’ green practices can help create unique competitive advantages , such asThe exhibition industry is a huge industry. In 2018, there were approximately 32,000 exhibitions directly involving 303 million visitors and nearly five million exhibitors across more than 180 countries . The envChina’s exhibition industry has developed rapidly in recent years and has ranked first in the world in terms of scale . HoweverIn order to bridge the above research gaps, this research aims to achieve the following research goals: (1) Explore audiences’ perceptions of green practices in exhibitions and their impact on exhibition brand loyalty; (2) Based on the commitment-trust theory, verify the green trust commitment framework’s role between audiences’ perceptions of green practices and exhibition brand loyalty; and (3) Explore the moderating effect of audiences’ green self-identity in the green trust-commitment framework.Green practices have been valued in many industries. The exhibition industry is an industry that has a significant impact on the environment. For example, the internationally-renowned organization However, there is very little research on the green operation of exhibitions from the perspective of audiences. In the field of event management, few scholars have discussed the green practice perceptions of events such as festivals and conferences. For example, the green practice perceptions of festival participants are defined from the following three facets: green food; green environment and activity; and green design and waste management . The greTrust refers to the willingness to rely on an exchange partner in whom one has confidence . The conWith people’s emphasis on green marketing, H1: There is a significant positive relationship between green practices and green trust.Consumers’ commitment to a brand can be regarded as an emotional or psychological attachment , which rAccording to the Commitment-Trust Theory, there is a circular relationship between trust and commitment–that is, trust leads to commitment, and commitment strengthens trust. Nevertheless, the influence of trust on commitment should be an earlier relationship . The resH2: There is a significant positive relationship between green trust and affective commitment;H3: There is a significant positive relationship between green trust and social compliance commitment.Brand loyalty can be defined as the actual pattern of consumers’ purchase behavior for a certain brand , or the Many scholars have pointed out that brand trust is an important antecedent of brand loyalty i.e., . This isFor this reason, this research proposes the following hypotheses:H4: There is a significant positive relationship between green trust and brand loyalty;H5: There is a significant positive relationship between affective commitment and brand loyalty;H6: There is a significant positive relationship between social compliance commitment and brand loyalty;H7: There is a significant positive relationship between green practices and brand loyalty.Shared values have an important influence on trust and relationship commitment . The so-H8: Green consumer identity has a moderating effect on the green trust-affective commitment relationship;H9: Green consumer identity has a moderating effect on the green trust-social compliance commitment relationship.The theoretical framework of this research is shown in This research is divided into two stages. The main purpose of the first stage is to initially construct the dimension of audiences’ perceptions of green practices in exhibitions. In this stage, items were developed using a combination of literature review and in-depth interviews. Researchers conducted a survey of the audience during the 25th Macao International Trade and Investment Fair and conducted a factor analysis of green practice perceptions. In the first phase of the survey, a total of 350 questionnaires were distributed to the audience and 319 valid questionnaires were returned. The main purpose of the second stage is to perform a confirmatory factor analysis of the measurement tools of audiences’ perceptions of green practices, and to verify the theoretical model as well. The survey at this stage was mainly aimed at the audience of the China International Furniture Fair (Guangzhou) in 2021. A total of 400 questionnaires were issued, and 346 valid ones were collected, demographic information is shown in One of the purposes of this research is to explore audiences’ perceptions of green practices in exhibitions. For this reason, this study constructed the green practice audience perception measurement scale according to the method suggested by Green trust was measured using five items . ComposiThis research used SPSS23 for descriptive analysis and factor analysis in the development of measurement tools, and Amos 24 for CFA analysis and SEM analysis. When testing the theoretical model, researchers first checked the kurtosis, skewness, average value and other indicators, and then carried out a common method deviation test to judge whether the data could be further analyzed, after which they analyzed the total correlation among the items of each construct, eliminated inappropriate items, and then checked the reliability. CFA analysis, combination reliability analysis and convergence validity analysis were performed on the measurement model with Amos24.0. On this basis, SEM was used to test the overall structure model and verify the aforementioned hypotheses. This study adopted the bootstrap method to test the mediation effect. The moderated ordinary least squares regression analysis  method wBefore data analysis, this study checked the missing values and outliers of the data set and found that there were no missing values and outliers. Through kurtosis and skewness indicators, it can be found that the data is normally distributed and suitable for further analysis. Please refer to the attachment for specific data.In this study, In this study, SPSS23.0 was used to measure the reliability of each construct in the data set to ensure good consistency in each dimension. Through testing, the Cronbach’s alpha of each construct is between 0.790 and 0.903 see , and theThe square root of the AVE of each construct in this study is greater than the Pearson correlation coefficient of the construct and the related construct (see This study used the maximum likelihood method to test the hypotheses. Judging from the results of t = 3.413). For this reason, it can be concluded that the relationship between green trust and affective commitment will be moderated by green self-identity, so H8 has been verified. On the other path, the moderating effect of green self-identity is not significant, and H9 is not supported.The Hayes process (version 3) was adopted to verify the moderating effect of Green self-identity on the relationship between green trust and affective commitment and social commitment. The data in In order to further demonstrate the specific performance of the moderating effect, this studydrew simple slope, as shown in This study explores the dimension of green practice perceptions in the exhibition industry from the perspective of audiences, uses the trust-commitment theory to verify the relationship between green practice perceptions and exhibition brand loyalty, and draws some enlightening conclusions.First of all, from the perceptions of exhibition audiences, the green practices in exhibitions can be divided into five dimensions, namely technology-based consumption reduction (TCR); reduction of exhibition supplies (RES); recycling and reducing emissions (RRE), green food service (GFB), and strengthening the concept of sustainability (SCS). This result is consistent with the connotation of the concept of environmental sustainability proposed by Second, in terms of the impact of audiences’ perceptions of green practices on green trust, among the five dimensions, four dimensions have a significant impact on green trust, with the exception of RES. This result is not surprising, because in recent years, scholars in the hotel field have also questioned whether green behavior can really guarantee a positive response from consumers . For exaFrom the perspective of the relationship between green practice perceptions and exhibition brand loyalty, neither RES, RRE, nor GFB can establish a significant positive relationship with exhibition brand loyalty. The author believes that the uniqueness seeking theory  can explThird, from the perspective of moderating effect of self-identity, green self-identity only has a significant effect on the relationship between green trust and affective commitment but has no significant effect on the relationship between green trust and social compliance commitment. The results show that respondents with higher green self-identity have stronger green trust-affective commitment. This is because when the image of a product is consistent with individuals’ self-concept, it is easier to gain the favor of consumers , therebyAs the exhibition industry has not yet established a unified standard and identification system with a high degree of recognition in the green level of exhibitions, the choice of green brands for exhibition projects is not as obvious as personal environmental behavior in daily life. For this reason, there is no basis for important people around audiences to influence their brand commitment. Therefore, regardless of whether audiences identify themselves with green consumers, the relationship between environmental trust and social compliance commitment is not affected. This finding is also consistent to a certain extent with what The findings of this research also have some inspiration for exhibition management. First of all, as far as exhibition organizers are concerned, actively promoting green practices is not only a manifestation of the organizers’ strong sense of social responsibility, but also helps the audiences to form a certain brand loyalty to the exhibition projects. In particular, the combination of technology and environmental protection and the enhancement of the audiences’ environmental awareness can not only directly increase the brand loyalty to the projects, but also increase the loyalty by enhancing the audiences’ green trust and brand commitment. As for the reduction of pollutant emissions through recycling, exhibition organizers should focus on taking measures to strengthen the link between relevant measures and green trust of audiences. For example, the actual effect of waste recycling can be made explicit, such as notifying audiences of the amount of emission reduction achieved through the recycling process. It is also possible to consider enhancing the association between waste recycling and individuals, such as the association of resource recycling behavior with the personal identities of exhibition participants. In this way, the trust-commitment mechanism can be used to transform the green practices of recycling and emission reduction into brand loyalty in the exhibition industry. In terms of reducing the supply of exhibition materials, organizers should avoid letting audiences form the wrong sense of reducing costs in the name of environmental protection. To this end, organizers can consider establishing multiple communication channels with audiences on relevant green practices to help them build a positive sense of green trust. Secondly, this study also found that the relationship between green trust and social compliance commitment is relatively weak, and that green self-identity does not significantly moderate the above relationship. In view of this, exhibition organizers may also consider actively promoting exhibition projects to pass green certification, so as to let the public understand the environmental protection contributions of the exhibition projects, thereby further enhancing the association between green trust and social compliance commitment, and improving the conversion efficiency from green practices to brand loyalty. Finally, this study found that green self-identity has a significant moderating effect on the relationship between green trust and affective commitment. For this reason, exhibition organizers can also consider enhancing audiences’ green self-identity, so that a series of green practices in exhibitions can enhance audience loyalty to exhibition brands through a stronger trust-commitment framework.Despite some enlightening conclusions, this study also has the following limitations: First, this study’s measurement of audiences’ perceptions of exhibitions’ green practices comes from a self-developed study. Although its reliability and validity have been tested by two exhibition surveys, considering the diversity of exhibition types and content, the universality of exhibition audiences’ perceptions of green practices needs further testing.Second, in the research process, only a few exhibition projects in Guangzhou and Macau are involved. Due to the cultural differences in people’s perceptions of environmental impacts, the conclusions of related research may also need to be verified in a broader scope.Third, this research only explores the relationship between the perceptions of green practices and brand loyalty in the exhibition industry from the perspective of audiences. However, there are still many factors that affect audiences’ exhibition brand loyalty, such as the service quality of exhibitions, the service efficiency of exhibitions, etc. Future research can consider integrating these factors and green practices into the model for research.The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.