Privacy Risks of AI-Enabled Connected Vehicle Systems

Abstract

Artificial intelligence-enabled connected vehicles increasingly operate as distributed computational platforms that continuously acquire, infer, process, retain, and transmit information about drivers, passengers, surrounding road users, vehicles, and locations. While these capabilities enable navigation, driver assistance, predictive maintenance, personalization, automated safety functions, intelligent transportation services, and increasingly sophisticated vehicle automation, they also produce significant privacy risks. Precise geolocation, driving behavior, biometric information, voice recordings, infotainment contacts, camera imagery, diagnostic information, and derived behavioral profiles may expose individuals to persistent tracking, secondary data use, re-identification, discriminatory profiling, unauthorized disclosure, and cybersecurity-related privacy breaches. This study develops a Privacy-by-Design Governance Framework for AI-Enabled Connected Vehicles and evaluates its potential through a reproducible simulation-based audit.

Four privacy-sensitive data classes were modeled across 1,600 synthetic vehicle-data processing scenarios. Baseline privacy-exposure scores ranged from 61 to 82 on a 100-point risk scale, with precise location producing the highest modeled exposure. Applying data minimization, edge processing, purpose limitation, affirmative control mechanisms, encryption, access restrictions, and lifecycle deletion reduced average modeled privacy exposure by approximately 58%. In a cumulative governance scenario, simulated re-identification probability declined from 37.8% to 7.4%, while unauthorized-disclosure probability declined from 24.5% to 4.9%. The findings demonstrate that connected-vehicle  

Artificial intelligence-enabled connected vehicles increasingly operate as distributed computational platforms that continuously acquire, infer, process, retain, and transmit information about drivers, passengers, surrounding road users, vehicles, and locations. While these capabilities enable navigation, driver assistance, predictive maintenance, personalization, automated safety functions, intelligent transportation services, and increasingly sophisticated vehicle automation, they also produce significant privacy risks. Precise geolocation, driving behavior, biometric information, voice recordings, infotainment contacts, camera imagery, diagnostic information, and derived behavioral profiles may expose individuals to persistent tracking, secondary data use, re-identification, discriminatory profiling, unauthorized disclosure, and cybersecurity-related privacy breaches. This study develops a Privacy-by-Design Governance Framework for AI-Enabled Connected Vehicles and evaluates its potential through a reproducible simulation-based audit.

Four privacy-sensitive data classes were modeled across 1,600 synthetic vehicle-data processing scenarios. Baseline privacy-exposure scores ranged from 61 to 82 on a 100-point risk scale, with precise location producing the highest modeled exposure. Applying data minimization, edge processing, purpose limitation, affirmative control mechanisms, encryption, access restrictions, and lifecycle deletion reduced average modeled privacy exposure by approximately 58%. In a cumulative governance scenario, simulated re-identification probability declined from 37.8% to 7.4%, while unauthorized-disclosure probability declined from 24.5% to 4.9%. The findings demonstrate that connected-vehicle