NDLI: Bottom-up generalization: a data mining solution to privacy protection

Content Provider	IEEE Xplore Digital Library
Author	Ke Wang Yu, P.S. Chakraborty, S.
Copyright Year	2004
Description	Author affiliation: Simon Fraser Univ., Burnaby, BC, Canada (Ke Wang)
Abstract	The well-known privacy-preserved data mining modifies existing data mining techniques to randomized data. In this paper, we investigate data mining as a technique for masking data, therefore, termed data mining based privacy protection. This approach incorporates partially the requirement of a targeted data mining task into the process of masking data so that essential structure is preserved in the masked data. The idea is simple but novel: we explore the data generalization concept from data mining as a way to hide detailed information, rather than discover trends and patterns. Once the data is masked, standard data mining techniques can be applied without modification. Our work demonstrated another positive use of data mining technology: not only can it discover useful patterns, but also mask private information. We consider the following privacy problem: a data holder wants to release a version of data for building classification models, but wants to protect against linking the released data to an external source for inferring sensitive information. We adapt an iterative bottom-up generalization from data mining to generalize the data. The generalized data remains useful to classification but becomes difficult to link to other sources. The generalization space is specified by a hierarchical structure of generalizations. A key is identifying the best generalization to climb up the hierarchy at each iteration. Enumerating all candidate generalizations is impractical. We present a scalable solution that examines at most one generalization in each iteration for each attribute involved in the linking.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Computational Intelligence (TCCI) IEEE Comput. Soc. Tech. Comm. on Pattern Analysis and Machine Intelligence (TCPAMI)
Starting Page	249
Ending Page	256
File Size	180712
Page Count	8
File Format	PDF
ISBN	0769521428
DOI	10.1109/ICDM.2004.10110
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-11-01
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Data privacy Protection Joining processes Couplings Iris Biomedical engineering Councils Chemical engineering
Content Type	Text
Resource Type	Article

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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