Using unknowns to prevent discovery of association rules

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Using unknowns to prevent discovery of association rules

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ACM SIGMOD Record archive
Volume 30 , Issue 4 (December 2001) table of contents

SPECIAL ISSUE: Special section on data mining for intrusion detection and threat analysis table of contents

Pages: 45 - 54

Year of Publication: 2001

ISSN:0163-5808

Authors

Yücel Saygin	Bilkent University, Turkey
Vassilios S. Verykios	Drexel University
Chris Clifton	Purdue University

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 8, Downloads (12 Months): 60, Citation Count: 29

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ABSTRACT

Data mining technology has given us new capabilities to identify correlations in large data sets. This introduces risks when the data is to be made public, but the correlations are private. We introduce a method for selectively removing individual values from a database to prevent the discovery of a set of rules, while preserving the data for other applications. The efficacy and complexity of this method are discussed. We also present an experiment showing an example of this methodology.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Dakshi Agrawal , Charu C. Aggarwal, On the design and quantification of privacy preserving data mining algorithms, Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, p.247-255, May 2001, Santa Barbara, California, United States [doi>10.1145/375551.375602]
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	6	Elena Dasseni , Vassilios S. Verykios , Ahmed K. Elmagarmid , Elisa Bertino, Hiding Association Rules by Using Confidence and Support, Proceedings of the 4th International Workshop on Information Hiding, p.369-383, April 25-27, 2001
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	8	U. of California at Irvine Machine Learning Repository. http://www.ics.uci.edu/~mlearn/MLSummary.html.
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CITED BY 29

	Tom Johnsten , Vijay V. Raghavan, A methodology for hiding knowledge in databases, Proceedings of the IEEE international conference on Privacy, security and data mining, p.9-17, December 01, 2002, Maebashi City, Japan
	Stanley R. M. Oliveira , Osmar R. Zaïane, Privacy preserving frequent itemset mining, Proceedings of the IEEE international conference on Privacy, security and data mining, p.43-54, December 01, 2002, Maebashi City, Japan
	Stanley R. M. Oliveira , Osmar R. Zaïane, Foundations for an access control model for privacy preservation in multi-relational association rule mining, Proceedings of the IEEE international conference on Privacy, security and data mining, p.19-26, December 01, 2002, Maebashi City, Japan
	Emmanuel D. Pontikakis , Yannis Theodoridis , Achilleas A. Tsitsonis , Liwu Chang , Vassilios S. Verykios, A quantitative and qualitative ANALYSIS of blocking in association rule hiding, Proceedings of the 2004 ACM workshop on Privacy in the electronic society, October 28-28, 2004, Washington DC, USA
	Vassilios S. Verykios , Elisa Bertino , Igor Nai Fovino , Loredana Parasiliti Provenza , Yucel Saygin , Yannis Theodoridis, State-of-the-art in privacy preserving data mining, ACM SIGMOD Record, v.33 n.1, March 2004
	Elaine M. Newton , Latanya Sweeney , Bradley Malin, Preserving Privacy by De-Identifying Face Images, IEEE Transactions on Knowledge and Data Engineering, v.17 n.2, p.232-243, February 2005
	Juggapong Natwichai , Xue Li , Maria E. Orlowska, A reconstruction-based algorithm for classification rules hiding, Proceedings of the 17th Australasian Database Conference, p.49-58, January 16-19, 2006, Hobart, Australia
	Ling Qiu , Yingjiu Li , Xintao Wu, Preserving privacy in association rule mining with bloom filters, Journal of Intelligent Information Systems, v.29 n.3, p.253-278, December 2007
	Da-Wei Wang , Churn-Jung Liau , Tsan-sheng Hsu, An epistemic framework for privacy protection in database linking, Data & Knowledge Engineering, v.61 n.1, p.176-205, April, 2007
	Shyue-Liang Wang , Bhavesh Parikh , Ayat Jafari, Hiding informative association rule sets, Expert Systems with Applications: An International Journal, v.33 n.2, p.316-323, August, 2007
	Ling Qiu , Kok-Leong Ong , Siu Man Lui, Safely delegating data mining tasks, Proceedings of the fifth Australasian conference on Data mining and analystics, p.1-7, November 29-30, 2006, Sydney, Australia
	Ali Amiri, Dare to share: Protecting sensitive knowledge with data sanitization, Decision Support Systems, v.43 n.1, p.181-191, February, 2007
	Shariq J. Rizvi , Jayant R. Haritsa, Maintaining data privacy in association rule mining, Proceedings of the 28th international conference on Very Large Data Bases, p.682-693, August 20-23, 2002, Hong Kong, China
	Syam Menon , Sumit Sarkar , Shibnath Mukherjee, Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns, Information Systems Research, v.16 n.3, p.256-270, September 2005
	Yu-Chiang Li , Jieh-Shan Yeh , Chin-Chen Chang, MICF: An effective sanitization algorithm for hiding sensitive patterns on data mining, Advanced Engineering Informatics, v.21 n.3, p.269-280, July, 2007
	Jun-Lin Lin , Julie Yu-Chih Liu, Privacy preserving itemset mining through fake transactions, Proceedings of the 2007 ACM symposium on Applied computing, March 11-15, 2007, Seoul, Korea
	Shyue-Liang Wang , Dipen Patel , Ayat Jafari , Tzung-Pei Hong, Hiding collaborative recommendation association rules, Applied Intelligence, v.27 n.1, p.67-77, August 2007
	Vassilios S. Verykios , Emmanuel D. Pontikakis , Yannis Theodoridis , Liwu Chang, Efficient algorithms for distortion and blocking techniques in association rule hiding, Distributed and Parallel Databases, v.22 n.1, p.85-104, August 2007
	Kun Liu , Hillol Kargupta , Jessica Ryan, Random Projection-Based Multiplicative Data Perturbation for Privacy Preserving Distributed Data Mining, IEEE Transactions on Knowledge and Data Engineering, v.18 n.1, p.92-106, January 2006
	Shipra Agrawal , Jayant R. Haritsa , B. Aditya Prakash, FRAPP: a framework for high-accuracy privacy-preserving mining, Data Mining and Knowledge Discovery, v.18 n.1, p.101-139, February 2009
	Juggapong Natwichai , Xingzhi Sun , Xue Li, Data reduction approach for sensitive associative classification rule hiding, Proceedings of the nineteenth conference on Australasian database, December 03-04, 2007, Gold Coast, Australia
	George V. Moustakides , Vassilios S. Verykios, A MaxMin approach for hiding frequent itemsets, Data & Knowledge Engineering, v.65 n.1, p.75-89, April, 2008
	Stanley R. M. Oliveira , Osmar R. Zaiane, A unified framework for protecting sensitive association rules in business collaboration, International Journal of Business Intelligence and Data Mining, v.1 n.3, p.247-287, March 2006
	Shyue-Liang Wang , Rajeev Maskey , Ayat Jafari , Tzung-Pei Hong, Efficient sanitization of informative association rules, Expert Systems with Applications: An International Journal, v.35 n.1-2, p.442-450, July, 2008
	Maurizio Atzori , Francesco Bonchi , Fosca Giannotti , Dino Pedreschi, Anonymity preserving pattern discovery, The VLDB Journal — The International Journal on Very Large Data Bases, v.17 n.4, p.703-727, July 2008
	Yi-Hung Wu , Chia-Ming Chiang , Arbee L. P. Chen, Hiding Sensitive Association Rules with Limited Side Effects, IEEE Transactions on Knowledge and Data Engineering, v.19 n.1, p.29-42, January 2007
	Shyue-Liang Wang, Maintenance of sanitizing informative association rules, Expert Systems with Applications: An International Journal, v.36 n.2, p.4006-4012, March, 2009
	Jun-Lin Lin , Yung-Wei Cheng, Privacy preserving itemset mining through noisy items, Expert Systems with Applications: An International Journal, v.36 n.3, p.5711-5717, April, 2009
	Ling Qiu , Yingjiu Li , Xintao Wu, Protecting business intelligence and customer privacy while outsourcing data mining tasks, Knowledge and Information Systems, v.17 n.1, p.99-120, October 2008