Homomorphic Encryption-Enabled Federated Learning for Privacy-Preserving Intrusion Detection in Resource-Constrained IoV Networks

TL;DR

This paper introduces a homomorphic encryption-based federated learning framework for privacy-preserving intrusion detection in resource-limited Internet-of-Vehicles networks, enabling secure data processing without significant performance loss.

Contribution

It presents a novel framework combining homomorphic encryption with federated learning tailored for IoV systems with limited resources, ensuring privacy and efficiency.

Findings

Achieves near non-encrypted performance with less than 0.8% accuracy gap.

Enables direct computation on encrypted data without decryption.

Improves privacy and efficiency in resource-constrained IoV intrusion detection.

Abstract

This paper aims to propose a novel framework to address the data privacy issue for Federated Learning (FL)-based Intrusion Detection Systems (IDSs) in Internet-of-Vehicles(IoVs) with limited computational resources. In particular, in conventional FL systems, it is usually assumed that the computing nodes have sufficient computational resources to process the training tasks. However, in practical IoV systems, vehicles usually have limited computational resources to process intensive training tasks, compromising the effectiveness of deploying FL in IDSs. While offloading data from vehicles to the cloud can mitigate this issue, it introduces significant privacy concerns for vehicle users (VUs). To resolve this issue, we first propose a highly-effective framework using homomorphic encryption to secure data that requires offloading to a centralized server for processing. Furthermore, we develop an effective training algorithm tailored to handle the challenges of FL-based systems with encrypted data. This algorithm allows the centralized server to directly compute on quantum-secure encrypted ciphertexts without needing decryption. This approach not only safeguards data privacy during the offloading process from VUs to the centralized server but also enhances the efficiency of utilizing FL for IDSs in IoV systems. Our simulation results show that our proposed approach can achieve a performance that is as close to that of the solution without encryption, with a gap of less than 0.8%.