Mobile Crowdsensing (MCS) has emerged as a powerful paradigm for large-scale data collection using mobile devices. However, traditional MCS frameworks pose significant privacy risks, particularly concerning worker identity and location disclosure during task execution and payment processing. Existing privacy-preserving approaches, such as dummy location insertion, k-anonymity, and differential privacy, either compromise efficiency or fail to address all privacy leakage vectors. To overcome these challenges, we propose a Zero-Knowledge Proof (ZKP)-based blockchain framework that ensures robust privacy protection while maintaining system efficiency. Our protocol leverages zk-SNARKs to protect worker identity and location during task submission and payment transactions. By integrating Ethereum smart contracts, we eliminate reliance on a centralized Crowdsensing Service Provider (CSP), mitigating single point of failure and Denial-of-Service (DoS) risks. We have provided formal security proofs for the transaction privacy. Through detailed experimentation on the Sepolia test network, we analyze gas costs and transaction finalization times, demonstrating that proof verification, despite its computational complexity, remains practical due to off-chain proof generation. The proposed framework optimizes on-chain and off-chain interactions, enhancing scalability while preserving user privacy. A comparative analysis against existing frameworks highlights our model’s superior privacy guarantees and computational efficiency.
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号