ETPTD: An Efficient Task-allocation and Privacy-preserving Truth Discovery Scheme in Crowdsensing
Yuanyuan Zhang, Bo Lu · 2025
With the widespread popularity of smart devices, mobile crowdsensing that encourages mobile users to participate in collaborative data collection has become a new trend for various IoT applications. As a result, truth discovery has received significant attention in performing sensing tasks. Although some current truth discovery systems can discover reliable truth from many conflicting data provided by users, there are still many privacy and efficiency issues that need to be addressed. Many existing systems either do not consider the privacy of users and other entities, or suffer from inefficiencies due to the computation of large amounts of sensing data. In addition, traditional privacy-preserving truth discovery systems face difficulties in task allocation, and there are problems in collecting higher quality data in sensing tasks. To address the above issues, we propose an efficient task-allocating and privacy-preserving crowdsensing truth discovery system. We use multi-party secret sharing to protect the user’s data privacy and weight privacy very well, and also protect the task truth value privacy with high efficiency.