Cloud-enabled privacy-preserving truth discovery in crowd sensing systems

The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems. However, the sen-sory data provided by individual participants are usually not reliable. To identify truthful values from the crowd sensing data, the topic of truth discovery, whose goal is to estimate user quality and infer truths through quality-aware data ag-gregation, has drawn significant attention. Though able to improve aggregation accuracy, existing truth discovery ap-proaches fail to take into consideration an important issue in their design, i.e., the protection of individual users ’ private information. In this paper, we propose a novel cloud-enabled privacy-preserving truth discovery (PPTD) framework for crowd sensing systems, which can achieve the protection of not only users ’ sensory data but also their reliability scores derived by the truth discovery approaches. The key idea of the proposed framework is to perform weighted aggrega-tion on users ’ encrypted data using homomorphic cryptosys-tem. In order to deal with large-scale data, we also propose to parallelize PPTD with MapReduce framework. Through extensive experiments on not only synthetic data but also real world crowd sensing systems, we justify the guarantee of strong privacy and high accuracy of our proposed frame-work