P2SimiDedup: Privacy-Preserving and Similarity-Based Deduplication Scheme for Fog-Assisted Vehicular Crowdsensing System

The rapid development of fog-assisted vehicular crowdsensing systems (FVCSs) enables real-time vehicular data sharing, but redundant and similar data in report results in unnecessary costs. However, previous studies only focus on duplicate reports and neglect deduplication of similar data. Besides, transmitting crowdsensing data in Internet of Vehicles (IoV) exposes vulnerabilities to offline brute-force and fake report attacks. In this article, we present P2SimiDedup, a scheme for secure deduplication of similar crowdsensing reports. Specifically, we develop cryptographic primitives and introduce an improved generalized deduplication technique (GreedyGD) to achieve secure deduplication over similar crowdsensing data. Then, we construct a two-level deduplication framework that can perform secure and efficient similar-based deduplication at fog nodes and cloud server. Besides, P2SimiDedup can ensure that only data requesters can decrypt and recover crowdsensing data. The security analysis and evaluation results demonstrate that P2SimiDedup can achieve privacy-preserving deduplication for similar crowdsensing reports with moderate computational, communication, and storage costs.