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Differentially Private K-Means Clustering

Authors:

Hongxia JinAuthors Info & Claims

CODASPY '16: Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy

Pages 26 - 37

https://doi.org/10.1145/2857705.2857708

Published: 09 March 2016 Publication History

Abstract

There are two broad approaches for differentially private data analysis. The interactive approach aims at developing customized differentially private algorithms for various data mining tasks. The non-interactive approach aims at developing differentially private algorithms that can output a synopsis of the input dataset, which can then be used to support various data mining tasks. In this paper we study the effectiveness of the two approaches on differentially private k-means clustering. We develop techniques to analyze the empirical error behaviors of the existing interactive and non-interactive approaches. Based on the analysis, we propose an improvement of DPLloyd which is a differentially private version of the Lloyd algorithm. We also propose a non-interactive approach EUGkM which publishes a differentially private synopsis for k-means clustering. Results from extensive and systematic experiments support our analysis and demonstrate the effectiveness of our improvement on DPLloyd and the proposed EUGkM algorithm.

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Cited By

Zhu YCao YXue QWu QZhang Y(2024)Heavy Hitter Identification Over Large-Domain Set-Valued Data With Local Differential PrivacyIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.332472619(414-426)Online publication date: 2024
https://doi.org/10.1109/TIFS.2023.3324726
Zhang MWang LZhang XWang YSun W(2024)Fully Privacy-Preserving and Efficient Clustering Scheme based on Fully Homomorphic EncryptionICC 2024 - IEEE International Conference on Communications10.1109/ICC51166.2024.10622837(2694-2700)Online publication date: 9-Jun-2024
https://doi.org/10.1109/ICC51166.2024.10622837
Wu FDu MZhi Q(2024)Density-Based Clustering with Differential PrivacyInformation Sciences10.1016/j.ins.2024.121211(121211)Online publication date: Jul-2024
https://doi.org/10.1016/j.ins.2024.121211
Show More Cited By

Index Terms

Differentially Private K-Means Clustering
1. Security and privacy
  1. Database and storage security
    1. Data anonymization and sanitization
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

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cover image ACM Conferences

CODASPY '16: Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy

March 2016

340 pages

ISBN:9781450339353

DOI:10.1145/2857705

General Chairs:
Elisa Bertino
Purdue University, USA
,
Ravi Sandhu
University of Texas at San Antonio, USA
,
Program Chair:
Alexander Pretschner
Technische Universität München, Germany

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 09 March 2016

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CODASPY'16

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CODASPY'16: Sixth ACM Conference on Data and Application Security and Privacy

March 9 - 11, 2016

Louisiana, New Orleans, USA

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CODASPY '16 Paper Acceptance Rate 22 of 115 submissions, 19%;

Overall Acceptance Rate 149 of 789 submissions, 19%

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Cited By

Zhu YCao YXue QWu QZhang Y(2024)Heavy Hitter Identification Over Large-Domain Set-Valued Data With Local Differential PrivacyIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.332472619(414-426)Online publication date: 2024
https://doi.org/10.1109/TIFS.2023.3324726
Zhang MWang LZhang XWang YSun W(2024)Fully Privacy-Preserving and Efficient Clustering Scheme based on Fully Homomorphic EncryptionICC 2024 - IEEE International Conference on Communications10.1109/ICC51166.2024.10622837(2694-2700)Online publication date: 9-Jun-2024
https://doi.org/10.1109/ICC51166.2024.10622837
Wu FDu MZhi Q(2024)Density-Based Clustering with Differential PrivacyInformation Sciences10.1016/j.ins.2024.121211(121211)Online publication date: Jul-2024
https://doi.org/10.1016/j.ins.2024.121211
Gehrke MLiebenow JMohammadi EBraun T(2024)Lifting in Support of Privacy-Preserving Probabilistic InferenceKI - Künstliche Intelligenz10.1007/s13218-024-00851-yOnline publication date: 13-Jun-2024
https://doi.org/10.1007/s13218-024-00851-y
Zhang MWang LZhang XLiu ZWang YBao H(2024)Efficient Clustering on Encrypted DataApplied Cryptography and Network Security10.1007/978-3-031-54770-6_9(213-236)Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-54770-6_9
Dalirrooyfard MMitrovic SNevmyvaka YOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Nearly tight bounds for differentially private multiway cutProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667206(24947-24965)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667206
Epasto AMirrokni VNarayanan SZhong POh ANaumann TGloberson ASaenko KHardt MLevine S(2023)k-means clustering with distance-based privacyProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666981(19570-19593)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666981
El-Gendy SElsayed MJurcut AAzer M(2023)Privacy Preservation Using Machine Learning in the Internet of ThingsMathematics10.3390/math1116347711:16(3477)Online publication date: 11-Aug-2023
https://doi.org/10.3390/math11163477
Li ZWang TLi N(2023)Differentially Private Vertical Federated ClusteringProceedings of the VLDB Endowment10.14778/3583140.358314616:6(1277-1290)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.14778/3583140.3583146
Chen LZeng LMu YChen L(2023)Global Combination and Clustering Based Differential Privacy Mixed Data PublishingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.323782235:11(11437-11448)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TKDE.2023.3237822
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