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.

With the popular usage of mobile devices and smartphones, crowd sensing becomes pervasive in real life when human acts as sensors to report their observations about entities. For the same entity, users may report conflicting informa-tion, and thus it is important to identify the true informa-tion and the reliable users. This task, referred to as truth discovery, has recently attracted much attention. Existing work typically assumes independence among entities. How-ever, correlations among entities are commonly observed in many applications. Such correlation information is crucial in the truth discovery task. When entities are not observed by enough reliable users, it is impossible to obtain true in-formation. In such cases, it is important to propagate trust-worthy information from correlated entities that have been observed by reliable users. We formulate the task of truth discovery on correlated entities as an optimization problem in which both truths and user reliability are modeled as vari-ables. The correlation among entities adds to the difficulty of solving this problem. In light of the challenge, we propose both sequential and parallel solutions. In the sequential so-lution, we partition entities into disjoint independent sets and derive iterative approaches based on block coordinate descent. In the parallel solution, we adapt the solution to MapReduce programming model, which can be executed on Hadoop clusters. Experiments on real-world crowd sensing applications show the advantages of the proposed method on discovering truths from conflicting information reported on correlated entities.