An Efficient Privacy Preservation Scheme with Blockchain-Based Reputation Management for IoV-Based Mobile Crowdsensing

Mobile crowdsensing (MCS) based on the Internet of Vehicles (IoV) has emerged as a viable solution for data collection. MCS utilizes a large number of mobile users to perform sensing tasks, thus reducing sensor deployment costs and improving data quality. However, IoV-based MCS faces two main challenges: privacy concerns and the presence of malicious vehicles. To address these challenges, this paper proposes an efficient privacy preservation scheme with blockchain-based reputation management (PPBRM) for Iov-based MCS. Specifically, we design an efficient sensing task allocation scheme that not only preserves location privacy, identity privacy, and sensing data privacy but also reduces computational and communication overhead. To identify malicious vehicles, ensure the security and reliability of sensing vehicle reputation values, and achieve efficient updates, we introduce a blockchain-based reputation management scheme suitable for IoV-based MCS, which fully considers the quality of sensing data, thereby ensuring the accuracy and fairness of reputation evaluation. Additionally, we introduce the concept of blockchain sharding and propose a multi-level graph partitioning-based blockchain sharding (MGPBS) method suitable for Iov-based MCS. MGPBS enhances the efficiency of blockchain reputation management by balancing the workload of each shard. The final simulation experiment results demonstrate the effectiveness and efficiency of the PPBRM scheme and MGPBS method.