Achieve Privacy-Preserving Truth Discovery in Crowdsensing Systems

Abstract

To solve the problem that the data collected in crowdsensing systems are not reliable, a large number of truth discovery protocols have been proposed. However, most of them neglect the privacy protection existing in crowdsensing systems. Some truth discovery protocols that consider privacy only provide limited privacy protection, such as only protecting the privacy of collected data. To bridge the gap, in this paper, we propose a more comprehensive privacy-preserving truth discovery protocol that can simultaneously protect the privacy of participants and truth results. Specifically, our protocol encrypts participants' observed data based on Paillier Homomorphic Cryptosystem. Then, through the interaction between two servers, we can calculate participants' weights and estimate the truth results in the encrypted domain. Moreover, based on the data perturbation technology, the privacy of sensitive data exchanged between the two servers is protected in our protocol. Theoretical analysis and experimental results demonstrate that our protocol can effectively protect the privacy of participants and truth results without losing the accuracy of truth results.