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Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
ISBN: 978-1-4244-5445-7
pp: 960-963
Ting Wang , College of Computing, Georgia Institute of Technology, USA
Ling Liu , College of Computing, Georgia Institute of Technology, USA
ABSTRACT
As a severe threat in anonymized data publication, proximity breach is gaining increasing attention. Such breach occurs when an attacker learns with high confidence that the sensitive information of a victim associates with a set of semantically proximate values, even though not sure about the exact one. Recently (∈, δ)-dissimilarity [14] has been proposed as an effective countermeasure against general proximity attack. In this paper, we present a detailed analytical study on the fulfillment of this principle, derive criteria to efficiently test its satisfiability for given microdata, and point to a novel anonymization model, XCOLOR, with theoretical guarantees on both operation efficiency and utility preservation.
CITATION
Ting Wang, Ling Liu, "XColor: Protecting general proximity privacy", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 960-963, doi:10.1109/ICDE.2010.5447910
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