A Survey on Privacy-Preserving Computing in the Automotive Domain

TL;DR

This survey reviews privacy-preserving computing techniques like MPC and HE in the automotive domain, highlighting their applications, challenges, and research gaps to promote further advancements in vehicle data privacy.

Contribution

It provides a comprehensive overview of privacy-preserving methods in automotive applications, identifying current use cases, challenges, and future research directions.

Findings

MPC and HE are applicable in various automotive privacy scenarios.

Current research faces challenges in scalability and real-world deployment.

The survey highlights gaps and opportunities for future work.

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.