Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2011

Abstract

Publishing individual specific microdata has serious privacy implications. The k-anonymity model has been proposed to prevent identity disclosure from microdata, and the work on ℓ-diversity and t-closeness attempt to address attribute disclosure. However, most current work only deal with publishing microdata with a single sensitive attribute (SA), whereas real life scenarios often involve microdata with multiple SAs that may be multi-valued. This paper explores the issue of attribute disclosure in such scenarios. We propose a method called CODIP (Complete Disjoint Projections) that outlines a general solution to deal with the shortcomings in a naïve approach. We also introduce two measures, Association Loss Ratio and Information Exposure Ratio, to quantify data quality and privacy, respectively. We further propose a heuristic CODIP*for CODIP, which obtains a good trade-off in data quality and privacy. Finally, initial experiments show that CODIP*is practically useful on varying numbers of SAs.

Keywords

Data quality, General solutions, K-Anonymity, Loss ratio, Microdata, Sensitive attribute, T-closeness

Discipline

Databases and Information Systems | Information Security

Research Areas

Data Science and Engineering

Publication

Database and expert systems applications: 22nd international conference, DEXA 2011, Toulouse, France, August 29 - September 2

First Page

187

Last Page

201

ISBN

9783642230875

Identifier

10.1007/978-3-642-23088-2_13

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/978-3-642-23088-2_13

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