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- Title
Providing k -anonymity in data mining.
- Authors
Arik Friedman; Ran Wolff; Assaf Schuster
- Abstract
Abstract In this paper we present extended definitions of k-anonymity and use them to prove that a given data mining model does not violate the k-anonymity of the individuals represented in the learning examples. Our extension provides a tool that measures the amount of anonymity retained during data mining. We show that our model can be applied to various data mining problems, such as classification, association rule mining and clustering. We describe two data mining algorithms which exploit our extension to guarantee they will generate only k-anonymous output, and provide experimental results for one of them. Finally, we show that our method contributes new and efficient ways to anonymize data and preserve patterns during anonymization.
- Subjects
DATA mining; ANONYMITY; DATA security; ONLINE data processing
- Publication
VLDB Journal International Journal on Very Large Data Bases, 2008, Vol 17, Issue 4, p789
- ISSN
1066-8888
- Publication type
Article
- DOI
10.1007/s00778-006-0039-5