As Connected and Autonomous Vehicles (CAVs) are increasingly integrated into our transportation systems, they generate and exchange substantial amounts of data. This data includes not only details about the vehicle and road conditions but also private information about drivers and passengers, The exchange of this data can enhance data analytics and decision-making, leading to more effective driving. However, it also brings significant privacy concerns regarding personal data exposure, data misuse, and security risks. The benefits of data sharing and analytics in CAVs would not persist, without strong safeguards that protect privacy. In Davos,an innovative integrated and comprehensive framework which can enable privacy-preserving data sharing and analytics among Connected and Autonomous Vehicles (CAVs.)
