Privacy-preserving publishing data with full functional dependencies

Hui Wang, Ruilin Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

We study the privacy threat by publishing data that contains full functional dependencies (FFDs). We show that the cross-attribute correlations by FFDs can bring potential vulnerability to privacy. Unfortunately, none of the existing anonymization principles can effectively prevent against the FFD-based privacy attack. In this paper, we formalize the FFD-based privacy attack, define the privacy model (d, ℓ)-inference to combat the FFD-based attack, and design robust anonymization algorithm that achieves (d, ℓ)-inference. The efficiency and effectiveness of our approach are demonstrated by the empirical study.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 15th International Conference, DASFAA 2010, Proceedings
Pages176-183
Number of pages8
EditionPART 2
DOIs
StatePublished - 2010
Event15th International Conference on Database Systems for Advanced Applications, DASFAA 2010 - Tsukuba, Japan
Duration: 1 Apr 20104 Apr 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5982 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Database Systems for Advanced Applications, DASFAA 2010
Country/TerritoryJapan
CityTsukuba
Period1/04/104/04/10

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