A Density Peaking Clustering Algorithm for Differential Privacy Preservation

• Published in: IEEE access Vol. 11; pp. 54240 - 54253
• Main Authors: Chen, Hua, Mei, Kehui, Zhou, Yuan, Wang, Nan, Tang, Mengdi, Cai, Guangxing
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Chebyshev approximation; Chebyshev distance; Cluster analysis; Clustering; Clustering algorithms; Data mining; Density; dichotomous method; Differential privacy; Euclidean geometry; Laplace mechanism; Partitioning algorithms; Privacy; Resource management; Sensitivity; Stability analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3281652

The privacy protection problem in data mining has received increasingly attention and is a hot topic of current research. To address the problems of large accuracy loss and instability of clustering results of clustering algorithms under differential privacy protection requirements, a density peak clustering algorithm for differential privacy protection (DP-chDPC) is proposed. Firstly, the original DPC algorithm is improved, by using the dichotomy method to automatically determine the truncation distance to avoid the subjectivity of manual selection, and by setting the threshold of local density and center offset distance to automatically obtain the clustering center, which overcomes the uncertainty of the original DPC algorithm to select the clustering center based on the decision graph. Then, noise is added to the local density by using the Laplace mechanism to realize the differential privacy protection of the algorithm during the clustering analysis. Finally, the Chebyshev distance is used to replace the Euclidean distance to calculate the distance matrix, which reduces the interference on the clustering results after the algorithm adds noise, and reduces the loss of clustering accuracy, so that the stability of the algorithm is improved. The experimental results show that the DP-chDPC algorithm can effectively reduce the loss of clustering accuracy after the algorithm adds noise, and the clustering results are more stable.