Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile Crowdsensing

In mobile crowdsensing, truth discovery (TD) enables a crowdsensing server to extract truthful information from possibly conflicting crowdsensing data. TD provides a more accurate truth estimation than traditional truth inference methods like majority voting and averaging. However, there still exist crucial data privacy (including sensory data, inferred truths, and intermediates) and practicability (e.g., efficiency, utility, and non-interaction) concerns in real-world crowdsensing applications. Existing researches either fail to provide adequate data privacy protection throughout the entire TD procedure or suffer from low practicability. In this paper, we propose two schemes: a basic privacy-aware TD scheme (BPTD) and a privacy-enhanced TD scheme (PETD) with two servers for mobile crowdsensing, comprehensively considering both privacy and practicability. BPTD is straightforwardly conducted on shared data with few user-side interactions, while achieving high efficiency. To further liberate mobile users and prevent disclosure of the intermediates, PETD incorporates a novel partial decryption-based Paillier Cryptosystem to work with secret sharing, offering enhanced privacy protection without relying on any user-side involvement. Additionally, we improve the efficiency of PETD via data packing. Security analysis shows the desired privacy goals. Compared to prior studies with the best security guarantees, our extensive experiments demonstrate a comparable and superior performance regarding different metrics.