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.

References

[1]

N. R. Adam and J. C. Wortman. Security-control methods for statistical databases. ACM Computing Surveys, 21(4):515--556, Dec. 1989.]]

Digital Library

[2]

D. Agrawal and C. C. Aggarwal. On the Design and Quantification of Privacy Preserving Data Mining Algorithms. In Proc. of the 20th ACM Symposium on Principles of Database Systems, pages 247--255, Santa Barbara, California, May 2001.]]

Digital Library

[3]

R. Agrawal. Data Mining: Crossing the Chasm. In 5th Int'l Conference on Knowledge Discovery in Databases and Data Mining, San Diego, California, August 1999. Available from http://www.almaden.ibm.com/cs/quest/papers/kdd99_chasm.ppt.]]

Digital Library

[4]

R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. of the ACM SIGMOD Conference on Management of Data, pages 207--216, Washington, D.C., May 1993.]]

Digital Library

[5]

R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast Discovery of Association Rules. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, chapter 12, pages 307--328. AAAI/MIT Press, 1996.]]

Digital Library

[6]

R. Agrawal and R. Srikant. Fast Algorithms for Mining Association Rules. Research Report RJ 9839, IBM Almaden Research Center, San Jose, California, June 1994.]]

[7]

R. Agrawal and R. Srikant. Privacy preserving data mining. In Proc. of the ACM SIGMOD Conference on Management of Data, pages 439--450, Dallas, Texas, May 2000.]]

Digital Library

[8]

R. Bayardo. Efficiently mining long patterns from databases. In Proc. of the ACM SIGMOD Conference on Management of Data, Seattle, Washington, 1998.]]

Digital Library

[9]

L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, Belmont, 1984.]]

[10]

Business Week. Privacy on the Net, March 2000.]]

[11]

C. Clifton and D. Marks. Security and privacy implications of data mining. In ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, pages 15--19, May 1996.]]

[12]

R. Conway and D. Strip. Selective partial access to a database. In Proc. ACM Annual Conf., pages 85--89, 1976.]]

Digital Library

[13]

L. Cranor, J. Reagle, and M. Ackerman. Beyond concern: Understanding net users' attitudes about online privacy. Technical Report TR 99.4.3, AT&T Labs--Research, April 1999.]]

[14]

L. F. Cranor, editor. Special Issue on Internet Privacy. Comm. ACM, 42(2), Feb. 1999.]]

Digital Library

[15]

The Economist. The End of Privacy, May 1999.]]

[16]

V. Estivill-Castro and L. Brankovic. Data swapping: Balancing privacy against precision in mining for logic rules. In M. Mohania and A. Tjoa, editors, Data Warehousing and Knowledge Discovery DaWaK-99, pages 389--398. Springer-Verlag Lecture Notes in Computer Science 1676, 1999.]]

Digital Library

[17]

European Union. Directive on Privacy Protection, October 1998.]]

[18]

Y. Lindell and B. Pinkas. Privacy preserving data mining. In CRYPTO, pages 36--54, 2000.]]

Digital Library

[19]

T. M. Mitchell. Machine Learning, chapter 6. McGraw-Hill, 1997.]]

Digital Library

[20]

Office of the Information and Privacy Commissioner, Ontario. Data Mining: Staking a Claim on Your Privacy, January 1998.]]

[21]

J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81--106, 1986.]]

Digital Library

[22]

A. Shoshani. Statistical databases: Characteristics, problems and some solutions. In VLDB, pages 208--213, Mexico City, Mexico, September 1982.]]

Digital Library

[23]

K. Thearling. Data mining and privacy: A conflict in making. DS*, March 1998.]]

[24]

Time. The Death of Privacy, August 1997.]]

[25]

J. Vaidya and C. W. Clifton. Privacy preserving association rule mining in vertically partitioned data. In Proc. of the 8th ACM SIGKDD Int'l Conference on Knowledge Discovery and Data Mining, Edmonton, Canada, July 2002.]]

Digital Library

[26]

S. Warner. Randomized response: A survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc., 60(309):63--69, March 1965.]]

[27]

A. Westin. E-commerce and privacy: What net users want. Technical report, Louis Harris & Associates, June 1998.]]

[28]

A. Westin. Privacy concerns & consumer choice. Technical report, Louis Harris & Associates, Dec. 1998.]]

[29]

A. Westin. Freebies and privacy: What net users think. Technical report, Opinion Research Corporation, July 1999.]]

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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