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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.

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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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Artioli ABedogni LAndreolini M(2024)Effects of Geohashing and K-Means Clustering on Uniqueness in a Mobility Dataset2024 IEEE/ACM Symposium on Edge Computing (SEC)10.1109/SEC62691.2024.00042(389-394)Online publication date: 4-Dec-2024
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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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Published: 14 June 2004

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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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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
Artioli ABedogni LAndreolini M(2024)Effects of Geohashing and K-Means Clustering on Uniqueness in a Mobility Dataset2024 IEEE/ACM Symposium on Edge Computing (SEC)10.1109/SEC62691.2024.00042(389-394)Online publication date: 4-Dec-2024
https://doi.org/10.1109/SEC62691.2024.00042
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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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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(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing

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k-anonymity: a model for protecting privacy

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