A Local Differential Privacy Based Privacy-Preserving Grid Clustering Method

With the continuous deepening of mobile Internet applications, a large number of individual data have been produced. Collecting data distributed on different terminals for clustering can find the behavior patterns of people and support the in-depth development of application services. However, these data often contain individual sensitive information. Directly collecting data for clustering has the risk of revealing individual data privacy in the case of a lack of trusted data collectors. In recent years, localized differential Privacy (local differential privacy, LDP) has been continuously concerned by researchers in privacy protection because of its rigorous mathematical theory. Most of the existing LDP-based clustering methods use partition-based clustering methods, which are only suitable for convex distribution data and have the problem of large clustering quality loss. To tackle these problems, we focus on grid clustering and propose a local differential privacy based privacy-preserving grid clustering method. Firstly, we design an evaluation index of grid division, which adjusts the grid density estimation error and the loss of cluster edge information to guide the selection of grid structure. Then, we construct a cyclic feedback mechanism between the server and the terminal, which uses data distribution information to iteratively optimize the disturbance granularity, reduce the amount of differential noise injection, and improve the accuracy of grid density estimation. Finally, we propose an adaptive grid aggregation method based on grid structure to improve the accuracy of privacy protection clustering on the server-side. Theoretical analysis and experimental results show that the proposed method considers the privacy of each terminal's individual data and has a good clustering effect on different distributed data. © 2023 Science Press. All rights reserved.