What is it about?

Connected and autonomous vehicles rely on massive data exchange among vehicles, infrastructure, and cloud systems. Ensuring privacy in these interactions is important for regulatory compliance and user trust. Our survey systematically examines cryptographic primitives, specifically Secure Multi-Party Computation and Homomorphic Encryption, applied to automotive applications such as vehicle data analytics, location-based services, mobility infrastructures, and traffic management. By analyzing over 60 studies, we classify solution approaches, security models, and performance characteristics, providing a clear reference for researchers and engineers working to integrate privacy-preserving computation into the automotive domain.

Featured Image

Why is it important?

By exploring a wide range of privacy-sensitive automotive use cases, this survey provides a comprehensive view of how data protection challenges emerge in connected vehicles. It is especially valuable for researchers and practitioners designing privacy-preserving automotive services. Through a detailed analysis of MPC and HE applications, we identify key trade-offs between efficiency and privacy and offer insights that guide the integration of privacy-preserving technologies into next-generation mobility systems.

Perspectives

Working on this survey gave me a clearer view of how privacy-preserving computing can move from theoretical cryptographic primitives to real, system-level applications in the automotive domain. What interested me most was uncovering how different research communities approach the same problem from distinct perspectives, security, performance, and practicality.

Nergiz Yuca
Universitat Passau

Read the Original

This page is a summary of: A Survey on Privacy-Preserving Computing in the Automotive Domain, ACM Computing Surveys, November 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3770580.
You can read the full text:

Read

Contributors

The following have contributed to this page