XColor: Protecting general proximity privacy

Ting Wang; Ling Liu

doi:10.1109/ICDE.2010.5447910

XColor: Protecting general proximity privacy

Ting Wang, Ling Liu

College of Information Sciences and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

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 (ε, 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.

Original language	English (US)
Title of host publication	26th IEEE International Conference on Data Engineering, ICDE 2010 - Conference Proceedings
Pages	960-963
Number of pages	4
DOIs	https://doi.org/10.1109/ICDE.2010.5447910
State	Published - Jun 1 2010
Event	26th IEEE International Conference on Data Engineering, ICDE 2010 - Long Beach, CA, United States Duration: Mar 1 2010 → Mar 6 2010

Publication series

Name	Proceedings - International Conference on Data Engineering
ISSN (Print)	1084-4627

Other

Other	26th IEEE International Conference on Data Engineering, ICDE 2010
Country/Territory	United States
City	Long Beach, CA
Period	3/1/10 → 3/6/10

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Information Systems

Access to Document

10.1109/ICDE.2010.5447910

Cite this

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title = "XColor: Protecting general proximity privacy",

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 (ε, 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.",

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year = "2010",

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series = "Proceedings - International Conference on Data Engineering",

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note = "26th IEEE International Conference on Data Engineering, ICDE 2010 ; Conference date: 01-03-2010 Through 06-03-2010",

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Wang, T & Liu, L 2010, XColor: Protecting general proximity privacy. in 26th IEEE International Conference on Data Engineering, ICDE 2010 - Conference Proceedings., 5447910, Proceedings - International Conference on Data Engineering, pp. 960-963, 26th IEEE International Conference on Data Engineering, ICDE 2010, Long Beach, CA, United States, 3/1/10. https://doi.org/10.1109/ICDE.2010.5447910

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AB - 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 (ε, 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.

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ER -

XColor: Protecting general proximity privacy

Abstract

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