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

IF 1.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yang Bai, Yutang Rao, Hongyan Wu, Juan Wang, Wentao Yang, Gaojie Xing, Jiawei Yang, Xiaoshu Yuan
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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.

Abstract Image

利用同态代理再加密增强基于联邦学习的智能网联车辆的安全性和隐私性
智能网联汽车(icv)是一个快速发展的方向,在自动驾驶领域发挥着重要作用。为了实现icv之间的协同计算,联邦学习(FL)或基于联邦的大语言模型(FedLLM)作为一种很有前途的分布式方法,已被用于支持icv场景中的各种协同应用计算,例如分析车辆驾驶信息以实现轨迹预测、语音激活控制、会话AI助手。不幸的是,最近的研究表明,FL系统仍然面临着来自诚实但好奇的服务器、诚实但好奇的分布式参与者或参与者与服务器之间的勾结的隐私挑战。这些威胁可能导致敏感的私人数据泄露,例如位置信息和驾驶条件。同态加密(HE)是一种典型的对模型精度影响较小的缓解方法,前人已经对其进行了研究。但是,单密钥HE不能抵抗参与者与服务器之间的合谋,多密钥HE不适合icv场景。在这项工作中,我们提出了一种将FL与基于参与者ID信息的同态代理重加密(PRE)相结合的新方法。通过这样做,基于fl的icv可以成功地抵御隐私威胁。理论分析和实验结果表明,基于id同态PRE的防御框架能够达到较高的安全级别和高效的计算。我们期望我们的方法可以作为一个基本点来支持对联邦法学硕士隐私保护的广泛研究。
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来源期刊
IET Information Security
IET Information Security 工程技术-计算机:理论方法
CiteScore
3.80
自引率
7.10%
发文量
47
审稿时长
8.6 months
期刊介绍: IET Information Security publishes original research papers in the following areas of information security and cryptography. Submitting authors should specify clearly in their covering statement the area into which their paper falls. Scope: Access Control and Database Security Ad-Hoc Network Aspects Anonymity and E-Voting Authentication Block Ciphers and Hash Functions Blockchain, Bitcoin (Technical aspects only) Broadcast Encryption and Traitor Tracing Combinatorial Aspects Covert Channels and Information Flow Critical Infrastructures Cryptanalysis Dependability Digital Rights Management Digital Signature Schemes Digital Steganography Economic Aspects of Information Security Elliptic Curve Cryptography and Number Theory Embedded Systems Aspects Embedded Systems Security and Forensics Financial Cryptography Firewall Security Formal Methods and Security Verification Human Aspects Information Warfare and Survivability Intrusion Detection Java and XML Security Key Distribution Key Management Malware Multi-Party Computation and Threshold Cryptography Peer-to-peer Security PKIs Public-Key and Hybrid Encryption Quantum Cryptography Risks of using Computers Robust Networks Secret Sharing Secure Electronic Commerce Software Obfuscation Stream Ciphers Trust Models Watermarking and Fingerprinting Special Issues. Current Call for Papers: Security on Mobile and IoT devices - https://digital-library.theiet.org/files/IET_IFS_SMID_CFP.pdf
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