Privacy Protection Sensing Data Aggregation for Crowd Sensing

Abstract

The emergence of the crowd sensing solves the problem that the traditional perception mode is hard to deploy on a large scale and at a high cost. However, users are exposed to the risk of privacy leakage when participating in crowd sensing. In order to solve this issue, this paper protects the user’s privacy through the dynamic group collaborative data submission mechanism and the method of adding noise perturbation, solves the privacy protection problem in the case of collusion attack. While implementing privacy protection and taking into consideration performance, this solution further reduces the cost of the system through batch verification. Safety analysis and simulation show the effectiveness and efficiency of the proposed method.