부산대학교 도서관

Privacy-preserving truth discovery, as a data aggregation algorithm that can extract reliable results from disparate and conflicting data in a privacy-preserving manner, has received a lot of attention in ensuring the reliability and privacy of data in mobile crowdsensing systems. However, most of the existing work requires that workers must stay online all the time during the full process of truth discovery. Although a few recent schemes have been proposed to tolerate worker dropout, they are tailored for a single-round setting. Repeating these schemes several times to adapt to the truth discovery will introduce significant computational and communication overheads, especially for the workers. To solve the above challenges, in this article, we propose a multiround efficient and secure truth discovery scheme in mobile crowdsensing systems that can balance the 3-way tradeoff between privacy protection, dropout tolerance, and protocol efficiency. Specifically, we devise a novel mask generation capable of reusing secrets to eliminate the costly overhead of workers needing to recompute new secrets each round. Besides, we design a lightweight dropout tolerance mechanism to guarantee that even if workers drop out halfway, the server can still acquire meaningful truth. Rigorous security analysis and extensive experimental results demonstrate the privacy and efficiency of our scheme, respectively.