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The introduction of advanced technologies has made driving a more automated activity. However, most vehicles are not designed with cybersecurity considerations and hence, they are susceptible to cyberattacks. When such incidents happen, it is critical for drivers to respond properly. The goal of this study was to observe drivers’ responses to unexpected vehicle cyberattacks while driving in a simulated environment and to gain deeper insights into their perceptions of vehicle cybersecurity. Ten participants completed the experiment and the results showed that they perceived and responded differently to each vehicle cyberattack. Participants correctly identified the cybersecurity issue and took according action when the issue caused a noticeable visual and auditory response. Participants preferred to be clearly informed about what happened and what to do through a combination of visual, tactile, and auditory warnings. The lack of knowledge of vehicle cybersecurity was obvious among participants. 

Secure multi-party computation (MPC) allows multiple parties to jointly compute the output of a function while preserving the privacy of any individual party's inputs to that function. As MPC protocols transition from research prototypes to real-world applications, the usability of MPC-enabled applications is increasingly critical to their successful deployment and wide adoption. Our Web-MPC platform, designed with a focus on usability, has been deployed for privacy-preserving data aggregation initiatives with the City of Boston and the Greater Boston Chamber of Commerce. After building and deploying an initial version of this platform, we conducted a heuristic evaluation to identify additional usability improvements and implemented corresponding application enhancements. However, it is difficult to gauge the effectiveness of these changes within the context of real-world deployments using traditional web analytics tools without compromising the security guarantees of the platform. This work consists of two contributions that address this challenge: (1) the Web-MPC platform has been extended with the capability to collect web analytics using existing MPC protocols, and (2) this capability has been leveraged to conduct a usability study comparing the two version of Web-MPC (before and after the heuristic evaluation and associated improvements). While many efforts have focused on ways to enhance the usability of privacy-preserving technologies, this study can serve as a model for using a privacy-preserving data-driven approach in evaluating or enhancing the usability of privacy-preserving websites and applications deployed in real-world scenarios. The data collected in this study yields insights about the interplay between usability and security that can help inform future implementations of applications that employ MPC.