Yunxuan Li

The digital revolution has led to significant technological advancements in the automotive industry, enabling vehicles to process and share information with other vehicles and the cloud. These data can be beneficial for achieving autonomous driving or creating safer road environments. However, they contain a significant amount of sensitive information that can be used to identify the underlying vehicle or construct a detailed profile of the driver. Thus, privacy concerns and issues are vital aspects of connected vehicle environments (CVEs).

Despite the drivers' desire to protect their sensitive data, their general demand is to continue utilizing as many connected vehicle-enabled applications (such as navigation or fatigue detection) as possible. Thus, a challenge of preserving privacy in CVEs is to balance the trade-off between privacy protection and service quality. Furthermore, research has shown that the sensitivity of personal data is related to when and where as well as for what purposes such information has been collected. This indicates that the privacy protection in CVEs must consider the dynamic and context-dependent nature of drivers' privacy needs. Lastly, to avoid the so-called "privacy paradox," where people claim to be concerned about their privacy but still share a lot of private information, user-friendly privacy management mechanisms should be developed.

The focus of my research is to develop a Situation-Aware Privacy-Preserving framework for Connected Vehicles that empowers drivers to manage their privacy preferences effectively while still harnessing the benefits of connected vehicle technology.