Using Homomorphic Proxy Re-Encryption to Enhance Security and Privacy of Federated Learning-Based Intelligent Connected Vehicles

Abstract

Intelligent connected vehicles (ICVs) are one of the fast-growing directions that plays a significant role in the area of autonomous driving. To realize collaborative computation among ICVs, federated learning (FL) or federated-based large language model (FedLLM) as a promising distributed approach has been used to support various collaborative application computations in ICVs scenarios, for example, analyzing vehicle driving information to realize trajectory prediction, voice-activated controls, conversational AI assistants. Unfortunately, recent research reveals that FL systems are still faced with privacy challenges from honest-but-curious server, honest-but-curious distributed participants, or the collusion between participants and the server. These threats can lead to the leakage of sensitive private data, such as location information and driving conditions. Homomorphic encryption (HE) is one of the typical mitigation that has few effects on the model accuracy and has been studied before. However, single-key HE cannot resist collusion between participants and the server, multikey HE is not suitable for ICVs scenarios. In this work, we proposed a novel approach that combines FL with homomorphic proxy re-encryption (PRE) which is based on participants’ ID information. By doing so, the FL-based ICVs can be able to successfully defend against privacy threats. In addition, we analyze the security and performance of our method, and the theoretical analysis and the experiment results show that our defense framework with ID-based homomorphic PRE can achieve a high-security level and efficient computation. We anticipate that our approach can serve as a fundamental point to support the extensive research on FedLLMs privacy-preserving.