DLLPM: Dual-layer location privacy matching in V2V energy trading

Abstract

The recent increase in Electric Vehicles (EVs) on the road has highlighted privacy concerns, particularly in the Vehicle-to-Vehicle (V2V) energy trading scenario. Ensuring location privacy in Vehicular Ad Hoc Networks (VANETs) is crucial for user confidentiality. Existing privacy techniques in the V2V paradigm protect the location coordinates of the EVs, but privacy risks persist after EVs are matched. In this paper, we introduce a dual-layer location privacy matching (DLLPM) technique to enhance the privacy of V2V matching. Our approach utilizes Laplace differential privacy and partial homomorphic encryption, ensuring that the EV’s private data remains inaccessible to both participants and adversaries. We introduce a noise addition and clipping algorithm to obfuscate EV coordinates within a defined radius. Encrypted distance-based preference lists are generated using partial homomorphic encryption to establish differentially private stable matches. DLLPM ensures EV location privacy throughout the matching process and mitigates the risk of location privacy leakage even after suppliers and demanders exchange location information. Theoretical analysis and experimental results confirm the efficiency of DLLPM, demonstrating robust privacy preservation with a computational complexity of $O(n^{2}logn\cdot (C_{\text{enc}}+C_{\text{addHE}}+C_{\text{subHE}}+C_{\text{dec}}))$. We further evaluate computational performance using 128-bit and 256-bit encryption, showing that DLLPM achieves private and efficient matching in the V2V trading paradigm.