Workload-aware anonymization

Authors:
Kristen LeFevre;David J. DeWitt;Raghu Ramakrishnan
Affiliations:
University of Wisconsin - Madison, Madison, WI;University of Wisconsin - Madison, Madison, WI;University of Wisconsin - Madison, Madison, WI & Yahoo! Research, Sunnyvale, CA
Venue:
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2006

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Abstract

Protecting data privacy is an important problem in microdata distribution. Anonymization algorithms typically aim to protect individual privacy, with minimal impact on the quality of the resulting data. While the bulk of previous work has measured quality through one-size-fits-all measures, we argue that quality is best judged with respect to the workload for which the data will ultimately be used.This paper provides a suite of anonymization algorithms that produce an anonymous view based on a target class of workloads, consisting of one or more data mining tasks, as well as selection predicates. An extensive experimental evaluation indicates that this approach is often more effective than previous anonymization techniques.