Article

On the complexity of optimal K-anonymity

Authors:

Adam Meyerson,

Ryan WilliamsAuthors Info & Claims

PODS '04: Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

Pages 223 - 228

https://doi.org/10.1145/1055558.1055591

Published: 14 June 2004 Publication History

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Abstract

The technique of k-anonymization has been proposed in the literature as an alternative way to release public information, while ensuring both data privacy and data integrity. We prove that two general versions of optimal k-anonymization of relations are NP-hard, including the suppression version which amounts to choosing a minimum number of entries to delete from the relation. We also present a polynomial time algorithm for optimal k-anonymity that achieves an approximation ratio independent of the size of the database, when k is constant. In particular, it is a O(k log k)-approximation where the constant in the big-O is no more than 4, However, the runtime of the algorithm is exponential in k. A slightly more clever algorithm removes this condition, but is a O(k log m)-approximation, where m is the degree of the relation. We believe this algorithm could potentially be quite fast in practice.

References

[1]

R. Agrawal, J. Kiernan, R. Srikant, and Y. Xu. Hippocratic databases. In Proc. of the 28th International Conference on Very Large Databases, 143--154, 2002.

Digital Library

Google Scholar

[2]

R. Agrawal and S. Ramakrishnan. Privacy Preserving Data Mining. In Proc. of ACM International Conference on Management of Data, 439--450, 2000.

Digital Library

Google Scholar

[3]

D. Agrawal and C. C. Aggarwal. On the design and quantification of privacy preserving data mining algorithms. In Proc. of ACM Symposium on Principles of Database Systems, 2001.

Digital Library

Google Scholar

[4]

I. Dinur and K. Nissim. Revealing Information while Preserving Privacy. In Proc. of ACM Symposium on Principles of Database Systems, 202--210, 2003.

Digital Library

Google Scholar

[5]

A. Evfimievski, J. E. Gehrke, and R. Srikant. Limiting Privacy Breaches in Privacy Preserving Data Mining. In Proc. of ACM Symposium on Principles of Database Systems, 211--222, 2003.

Digital Library

Google Scholar

[6]

D. S. Johnson. Approximation algorithms for combinatorial problems. Journal of Computer and System Sciences 9:256--278, 1974.

Digital Library

Google Scholar

[7]

J. Kleinberg, C. Papadimitriou, P. Raghavan. Auditing Boolean Attributes. In Proc. of ACM Symposium on Principles of Database Systems, 86--91, 2000.

Digital Library

Google Scholar

[8]

L. Sweeney. Optimal anonymity using k-similar, a new clustering algorithm. Under review, 2003.

Google Scholar

[9]

L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty,Fuzziness and Knowledge-based Systems 10(5), 557--570, 2002.

Digital Library

Google Scholar

[10]

P. Samarati and L. Sweeney. Generalizing Data to Provide Anonymity when Disclosing Information (Abstract). In Proc. of ACM Symposium on Principles of Database Systems, 188, 1998.

Digital Library

Google Scholar

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Berning SDunning VSpagnuelo DVeugen Tvan der Waa J(2024)The Trade-off Between Privacy & Quality for Counterfactual ExplanationsProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3670897(1-9)Online publication date: 30-Jul-2024
https://dl.acm.org/doi/10.1145/3664476.3670897
Sangaiah AJavadpour AJa'fari FPinto PChuang H(2024)Privacy-Aware and AI Techniques for Healthcare Based on K-Anonymity Model in Internet of ThingsIEEE Transactions on Engineering Management10.1109/TEM.2023.327159171(12448-12462)Online publication date: 2024
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Shyamasundar RMaurya M(2024)Anonymization of Bigdata using ARX Tools2024 15th International Conference on Information and Communication Systems (ICICS)10.1109/ICICS63486.2024.10638298(1-6)Online publication date: 13-Aug-2024
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Published In

PODS '04: Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

June 2004

350 pages

ISBN:158113858X

DOI:10.1145/1055558

Conference Chair:
Catriel Beeri
Hebrew University of Jerusalem

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

Publication History

Published: 14 June 2004

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SIGMOD/PODS04

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SIGMOD/PODS04: International Conference on Management of Data and Symposium on Principles Database and Systems

June 14 - 16, 2004

Paris, France

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Overall Acceptance Rate 642 of 2,707 submissions, 24%

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Berning SDunning VSpagnuelo DVeugen Tvan der Waa J(2024)The Trade-off Between Privacy & Quality for Counterfactual ExplanationsProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3670897(1-9)Online publication date: 30-Jul-2024
https://dl.acm.org/doi/10.1145/3664476.3670897
Sangaiah AJavadpour AJa'fari FPinto PChuang H(2024)Privacy-Aware and AI Techniques for Healthcare Based on K-Anonymity Model in Internet of ThingsIEEE Transactions on Engineering Management10.1109/TEM.2023.327159171(12448-12462)Online publication date: 2024
https://doi.org/10.1109/TEM.2023.3271591
Shyamasundar RMaurya M(2024)Anonymization of Bigdata using ARX Tools2024 15th International Conference on Information and Communication Systems (ICICS)10.1109/ICICS63486.2024.10638298(1-6)Online publication date: 13-Aug-2024
https://doi.org/10.1109/ICICS63486.2024.10638298
Canbay Y(2024)On the Complexity of Optimal k-Anonymity: A New Proof Based on Graph ColoringIEEE Access10.1109/ACCESS.2024.342439912(94197-94204)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3424399
Sowmyarani CNamya LNidhi GRamakanth Kumar P(2024)Score, Arrange, and Cluster: A Novel Clustering-Based Technique for Privacy-Preserving Data PublishingIEEE Access10.1109/ACCESS.2024.340337212(79861-79874)Online publication date: 2024
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Mayer RHittmeir MEkelhart A(2024)Distance-based linkage of personal microbiome records for identification and its privacy implicationsComputers and Security10.1016/j.cose.2023.103538136:COnline publication date: 1-Feb-2024
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Ge YWang HCao JZhang YJiang X(2024)Privacy-preserving data publishing: an information-driven distributed genetic algorithmWorld Wide Web10.1007/s11280-024-01241-y27:1Online publication date: 15-Jan-2024
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BORISOV ABOSOV AIVANOV A(2023)APPLICATION OF COMPUTER SIMULATION TO THE ANONYMIZATION OF PERSONAL DATA: STATE-OF-THE-ART AND KEY POINTSПрограммирование10.31857/S0132347423040040(58-74)Online publication date: 1-Jul-2023
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Goethals SSörensen KMartens D(2023)The Privacy Issue of Counterfactual Explanations: Explanation Linkage AttacksACM Transactions on Intelligent Systems and Technology10.1145/360848214:5(1-24)Online publication date: 11-Aug-2023
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