Towards comprehensive privacy protection in data clustering

Nan Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We address the protection of private information in data clustering. Previous work focuses on protecting the privacy of data being mined. We find that the cluster labels of individual data points can also be sensitive to data owners. Thus, we propose a privacy-preserving data clustering scheme that extracts accurate clustering rules from private data while protecting the privacy of both original data and individual cluster labels. We derive theoretical bounds on the performance of our scheme, and evaluate it experimentally with real-world data.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings
Pages1096-1104
Number of pages9
StatePublished - Dec 1 2007
Event11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007 - Nanjing, China
Duration: May 22 2007May 25 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4426 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007
CountryChina
CityNanjing
Period5/22/075/25/07

Fingerprint

Privacy Protection
Data Clustering
Labels
Data privacy
Privacy
Private Information
Privacy Preserving
Clustering
Evaluate

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhang, N. (2007). Towards comprehensive privacy protection in data clustering. In Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings (pp. 1096-1104). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4426 LNAI).
Zhang, Nan. / Towards comprehensive privacy protection in data clustering. Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings. 2007. pp. 1096-1104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "We address the protection of private information in data clustering. Previous work focuses on protecting the privacy of data being mined. We find that the cluster labels of individual data points can also be sensitive to data owners. Thus, we propose a privacy-preserving data clustering scheme that extracts accurate clustering rules from private data while protecting the privacy of both original data and individual cluster labels. We derive theoretical bounds on the performance of our scheme, and evaluate it experimentally with real-world data.",
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Zhang, N 2007, Towards comprehensive privacy protection in data clustering. in Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4426 LNAI, pp. 1096-1104, 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, Nanjing, China, 5/22/07.

Towards comprehensive privacy protection in data clustering. / Zhang, Nan.

Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings. 2007. p. 1096-1104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4426 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - We address the protection of private information in data clustering. Previous work focuses on protecting the privacy of data being mined. We find that the cluster labels of individual data points can also be sensitive to data owners. Thus, we propose a privacy-preserving data clustering scheme that extracts accurate clustering rules from private data while protecting the privacy of both original data and individual cluster labels. We derive theoretical bounds on the performance of our scheme, and evaluate it experimentally with real-world data.

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Zhang N. Towards comprehensive privacy protection in data clustering. In Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings. 2007. p. 1096-1104. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).