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Data Loss and Reconstruction of Location Differential Privacy Protection Based on Edge Computing

With the development of the Internet of Things (IoT), the delay caused by network transmission has led to low data processing efficiency. The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity. However, at the same time, the IoT faces important challenges, and edge computing uses a large number of distributed devices, making it difficult to perform centralized control. When an edge node is attacked, the attacker can continue to invade its connected nodes, thereby mining and stealing a user’s private data and causing losses. Once the edge layer communication link is attacked or accidentally interrupted, the user’s private information is likely to be leaked. To solve these problems, this paper proposes to protect user privacy by using differential privacy. First, according to the three-layer communication link structure of edge computing, a data query model is proposed; the main function is to capture the structure information and the data center connection weight and to query the connection relationship between the edge node and the client. Second, the edge node is regarded as the central server, and the differential privacy theory is used to realize the protection of location privacy. Finally, to reduce the data loss caused in the process of location protection, linear programming is adopted to realize the selection of the optimal location fuzzy matrix, and data loss and reconstruction methods are used to minimize the data uncertainty. In comparison to the existing differential privacy method, the method in this paper can achieve better privacy protection and can effectively reduce data loss.