CLDP=FATD: Secure Federated Averaging Threat Detection Framework for Intelligent Vehicle Sensor Networks Based on Client-Level Differential Privacy

The certification of real-time information in vehicles depends on threat detection. Intelligent vehicle sensor networks (IVSNs) have revolutionized modern transportation systems, enhancing traffic management and providing greater comfort. However, the increased use of smart sensing technologies has made connected and intelligent vehicles (CIVs) an attractive target for unauthorized access. Consequently, CIV owners are keen to ensure the security of their vehicle information, particularly the positioning, timing, and navigation of their vehicles. This article proposes a federated framework that utilizes client-level differential privacy (CLDP) to prevent privacy attacks, such as model inversion and membership inference attacks. In these attacks, an unauthorized party attempts to extract sensitive data from the model’s outputs to exploit its predictive capabilities. The CLDP-federated averaging threat detection (CLDP-FATD) approach utilizes Rényi-DP-Fed-Avg (RDP)/ $(\alpha, \epsilon)$ -DP, as an alternative to traditional DP algorithms to safeguard privacy and prevent data leakage within the federated learning (FL) framework. The efficacy of the proposed framework was evaluated using a GPS spoofing attack dataset. The findings demonstrate that the proposed scheme ensures collaborative privacy-utility tradeoff for CIV, achieving a minimal privacy budget $(\epsilon)$ of 0.99 at 94.27% and 2.0 at 88.42% for binary and multiclass, respectively, outperforming existing approaches.