Truth discovery techniques, which can obtain accurate aggregation results based on the weighted sensory data of users, are widely adopted in industrial sensing systems. However, there are some privacy matters that cannot be ignored in truth discovery process. While most of the existing privacy preserving truth discovery methods focus on the privacy of sensory data, they may neglect to protect the privacy of another equally important information, the tagged location information. In this paper, based on the three-layer architecture of edge computing, we introduce an efficient location privacy preserving truth discovery (ECo-LPPTD) scheme. By structuring data and applying homomorphic Paillier encryption, signcryption method, the privacy of location information and sensory data can be well protected in our scheme while the accuracy of aggregation can be guaranteed. Theoretical analysis and complete experiments performed on an industrial scenario demonstrate the efficiency of the proposed scheme.
The file is in txt format which can be read by programming languages such as python.