Privacy-Preserving Truth Discovery Based on Secure Multi-Party Computation in Vehicle-Based Mobile Crowdsensing

Vehicle-based mobile crowdsensing has gained widespread attention due to its low cost and efficient data collection mode. One common method to improve the accuracy of sensing data in this context is truth discovery. However, the emergence of privacy leakage and data misuse has reduced users’ motivation to participate in sensing tasks. Meanwhile, existing solutions for privacy-preserving truth discovery generally suffer from low computational efficiency and frequent interactions between users and servers. Hence, this paper proposes a novel privacy-preserving truth discovery scheme based on secure multi-party computation. For the purpose of high efficiency and strong privacy protection, we utilize the Secret Sharing method to securely decompose data and construct a Secure Multi-party Computation protocol to compute the ground truth. In addition, the weight value generated by truth discovery is employed as a quantitative data quality indicator that dynamically adjusts the user’s rewards and constructs a data quality-driven incentive mechanism. Finally, we demonstrate the high performance of our method through a detailed analysis, showing its effectiveness even in scenarios with numerous users.