Privacy-preserving data publishing

Ruilin Liu, Hui Wang

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

4 Scopus citations

Abstract

Data publishing has generated much concern on individual privacy. Recent work has focused on different background knowledge and their various threats to the privacy of published data. However, there still exist a few types of adversary knowledge waiting to be investigated. In this paper, I explain my research on privacy-preserving data publishing (PPDP) by using full functional dependencies (FFDs) as part of adversary knowledge. I also briefly explain my research plan

Original languageEnglish
Title of host publicationICDE Workshops 2010 - The 2010 IEEE 26th International Conference on Data Engineering Workshops
Pages305-308
Number of pages4
DOIs
StatePublished - 2010
Event2010 IEEE 26th International Conference on Data Engineering Workshops, ICDEW 2010 - Long Beach, CA, United States
Duration: 1 Mar 20106 Mar 2010

Publication series

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

Conference

Conference2010 IEEE 26th International Conference on Data Engineering Workshops, ICDEW 2010
Country/TerritoryUnited States
CityLong Beach, CA
Period1/03/106/03/10

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