A convergent differentially private k-means clustering algorithm

Zhigang Lu, Hong Shen

研究成果: Conference contribution同行評審

12 引文 斯高帕斯(Scopus)

摘要

Preserving differential privacy (DP) for the iterative clustering algorithms has been extensively studied in the interactive and the non-interactive settings. However, existing interactive differentially private clustering algorithms suffer from a non-convergence problem, i.e., these algorithms may not terminate without a predefined number of iterations. This problem severely impacts the clustering quality and the efficiency of the algorithm. To resolve this problem, we propose a novel iterative approach in the interactive settings which controls the orientation of the centroids movement over the iterations to ensure the convergence by injecting DP noise in a selected area. We prove that, in the expected case, our approach converges to the same centroids as Lloyd’s algorithm in at most twice the iterations of Lloyd’s algorithm. We perform experimental evaluations on real-world datasets to show that our algorithm outperforms the state-of-the-art of the interactive differentially private clustering algorithms with a guaranteed convergence and better clustering quality to meet the same DP requirement.

原文English
主出版物標題Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
編輯Zhi-Hua Zhou, Sheng-Jun Huang, Min-Ling Zhang, Zhiguo Gong, Qiang Yang
發行者Springer Verlag
頁面612-624
頁數13
ISBN(列印)9783030161477
DOIs
出版狀態Published - 2019
對外發佈
事件23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019 - Macau, China
持續時間: 14 4月 201917 4月 2019

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11439 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019
國家/地區China
城市Macau
期間14/04/1917/04/19

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