A Privacy-Preserving Mechanism Based on Local Differential Privacy in Edge Computing

With the development of Internet of Things(IoT), the delay caused by network transmission has led to low data processing efficiency. At the same time, the limited computing power and available energy consumption of IoT terminal devices are also the important bottlenecks that would restrict the application of blockchain, but edge computing could solve this problem. The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity. However, user data in edge computing is usually stored and processed in some honest-but-curious authorized entities, which leads to the leakage of users’ privacy information. In order to solve these problems, this paper proposes a location data collection method that satisfies the local differential privacy to protect users’ privacy. In this paper, a Voronoi diagram constructed by the Delaunay method is used to divide the road network space and determine the Voronoi grid region where the edge nodes are located. A random disturbance mechanism that satisfies the local differential privacy is utilized to disturb the original location data in each Voronoi grid. In addition, the effectiveness of the proposed privacy-preserving mechanism is verified through comparison experiments. Compared with the existing privacy-preserving methods, the proposed privacy-preserving mechanism can not only better meet users’ privacy needs, but also have higher data availability.