University of Windsor

Published purpose

Modern connected and autonomous vehicles (CAVs) incessantly gather and relay extensive personal information, including location history, driving patterns, biometric data, and infotainment choices, to enable AI-driven services like predictive maintenance, route optimization, and driver risk assessment. However, this rich data stream also exposes sensitive user information to potential misuse, unauthorized access, and breaches. The researchers note that in Canada, regulatory protections lag behind the technology. PIPEDA provides only general guidance, underscoring a pressing need for privacy-by-design solutions that both uphold consumer rights and anticipate upcoming legislation, such as the proposed Consumer Privacy Protection Act (CPPA). The project aims to address these challenges by presenting a privacy-preserving architecture utilizing homomorphic encryption (HE) and oblivious AI to enable encrypted data analytics in connected vehicles.