A Survey on Privacy-Preserving Computing in the Automotive Domain

2025-08-03

AI Summary

To address privacy risks arising from personal data sharing and processing in connected and autonomous vehicles (CAVs), this paper systematically surveys the state of the art and evolutionary trajectory of secure multi-party computation (MPC) and homomorphic encryption (HE) in vehicular applications—particularly location-based services and intelligent traffic management. As the first comprehensive study integrating privacy-preserving computation techniques into the automotive domain, it rigorously delineates the technical applicability boundaries of MPC and HE, identifying critical bottlenecks in computational efficiency, system scalability, and practical engineering deployment. Through multidimensional scenario analysis, the paper constructs a holistic taxonomy of privacy-enhancing computation technologies tailored to vehicular environments. It further articulates clear technology evolution pathways and optimization directions, thereby establishing a theoretical foundation for academic research and delivering an actionable, use-case-driven technology selection framework for industrial implementation.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Bias, Fairness & Privacy
• Application Domains: Mobility, Driving & Flight

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

As vehicles become increasingly connected and autonomous, they accumulate and manage various personal data, thereby presenting a key challenge in preserving privacy during data sharing and processing. This survey reviews applications of Secure Multi-Party Computation (MPC) and Homomorphic Encryption (HE) that address these privacy concerns in the automotive domain. First, we identify the scope of privacy-sensitive use cases for these technologies, by surveying existing works that address privacy issues in different automotive contexts, such as location-based services, mobility infrastructures, traffic management, etc. Then, we review recent works that employ MPC and HE as solutions for these use cases in detail. Our survey highlights the applicability of these privacy-preserving technologies in the automotive context, while also identifying challenges and gaps in the current research landscape. This work aims to provide a clear and comprehensive overview of this emerging field and to encourage further research in this domain.

Problem

• Addressing privacy concerns in connected and autonomous vehicles
• Reviewing MPC and HE applications for automotive data privacy
• Identifying gaps in privacy-preserving automotive research

Innovation

• Uses Secure Multi-Party Computation (MPC)
• Applies Homomorphic Encryption (HE)
• Targets automotive privacy-sensitive use cases