Article

Privacy preserving mining of association rules

Authors:

Alexandre Evfimievski,

Ramakrishnan Srikant,

Rakesh Agrawal,

Johannes GehrkeAuthors Info & Claims

KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 217 - 228

https://doi.org/10.1145/775047.775080

Published: 23 July 2002 Publication History

Abstract

We present a framework for mining association rules from transactions consisting of categorical items where the data has been randomized to preserve privacy of individual transactions. While it is feasible to recover association rules and preserve privacy using a straightforward "uniform" randomization, the discovered rules can unfortunately be exploited to find privacy breaches. We analyze the nature of privacy breaches and propose a class of randomization operators that are much more effective than uniform randomization in limiting the breaches. We derive formulae for an unbiased support estimator and its variance, which allow us to recover itemset supports from randomized datasets, and show how to incorporate these formulae into mining algorithms. Finally, we present experimental results that validate the algorithm by applying it on real datasets.

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Cited By

Wei FBao EXiao XYang YDing B(2024)AAA: An Adaptive Mechanism for Locally Differentially Private Mean EstimationProceedings of the VLDB Endowment10.14778/3659437.365944217:8(1843-1855)Online publication date: 31-May-2024
https://doi.org/10.14778/3659437.3659442
Barua EDutta M(2024)Privacy-preserving association rule mining: a survey of techniques for sensitive rule identification and enhanced data protectionInternational Journal of Computers and Applications10.1080/1206212X.2024.2307086(1-14)Online publication date: 29-Jan-2024
https://doi.org/10.1080/1206212X.2024.2307086
Roman A(2023)Evaluating the Privacy and Utility of Time-Series Data Perturbation AlgorithmsMathematics10.3390/math1105126011:5(1260)Online publication date: 5-Mar-2023
https://doi.org/10.3390/math11051260
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Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining

July 2002

719 pages

ISBN:158113567X

DOI:10.1145/775047

Conference Chair:
Osmar R. Zaïane
University of Alberta, Canada
,
General Chair:
Randy Goebel
University of Alberta, Canada
,
Program Chairs:
David Hand
Imperial College, UK
,
Daniel Keim
AT&T
,
Raymond Ng
University of British Columbia, Canada

Copyright © 2002 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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Publication History

Published: 23 July 2002

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KDD02: The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

July 23 - 26, 2002

Alberta, Edmonton, Canada

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KDD '02 Paper Acceptance Rate 44 of 307 submissions, 14%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Wei FBao EXiao XYang YDing B(2024)AAA: An Adaptive Mechanism for Locally Differentially Private Mean EstimationProceedings of the VLDB Endowment10.14778/3659437.365944217:8(1843-1855)Online publication date: 31-May-2024
https://doi.org/10.14778/3659437.3659442
Barua EDutta M(2024)Privacy-preserving association rule mining: a survey of techniques for sensitive rule identification and enhanced data protectionInternational Journal of Computers and Applications10.1080/1206212X.2024.2307086(1-14)Online publication date: 29-Jan-2024
https://doi.org/10.1080/1206212X.2024.2307086
Roman A(2023)Evaluating the Privacy and Utility of Time-Series Data Perturbation AlgorithmsMathematics10.3390/math1105126011:5(1260)Online publication date: 5-Mar-2023
https://doi.org/10.3390/math11051260
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
https://doi.org/10.31857/S0132347423040040
Borisov ABosov AIvanov A(2023)Application of Computer Simulation to the Anonymization of Personal Data: State-of-the-Art and Key PointsProgramming and Computer Software10.1134/S036176882304004749:4(232-246)Online publication date: 28-Jul-2023
https://doi.org/10.1134/S0361768823040047
Ergüner Özkoç E(2022)Privacy Preserving Data MiningData Mining - Concepts and Applictions10.5772/intechopen.99224Online publication date: 30-Mar-2022
https://doi.org/10.5772/intechopen.99224
Sumana M. Hareesha K. S. Kumar S(2021)Semantically Secure Classifiers for Privacy Preserving Data MiningResearch Anthology on Privatizing and Securing Data10.4018/978-1-7998-8954-0.ch049(1066-1095)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-8954-0.ch049
Mohan SAngamuthu T(2021)Association Rule Hiding in Privacy Preserving Data MiningResearch Anthology on Privatizing and Securing Data10.4018/978-1-7998-8954-0.ch044(963-986)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-8954-0.ch044
Bao EYang YXiao XDing B(2021)CGMProceedings of the VLDB Endowment10.14778/3476249.347627714:11(2258-2270)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476277
Wu TLin JZhang YChen C(2019)A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data MiningApplied Sciences10.3390/app90407749:4(774)Online publication date: 22-Feb-2019
https://doi.org/10.3390/app9040774
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