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Lyon
Aug. 22, 2011 to Aug. 27, 2011
ISBN: 978-1-4577-1373-6
pp: 253-256
ABSTRACT
Sharing data provides great benefit to the research community. But disclosing identifiable, sensitive information such as medical records can cause irreparable damage. A number of methods have been proposed to anon Mize sensitive information. With some approaches, term relationships in the data may help to re-identify the original data given the de-identified data. This papers studies the significance of correlation in data and then analyzes the effect on anonymization techniques including t-plausibility and k-manonymity. Finally, we show how to address correlation in thet-plausibility model.
INDEX TERMS
Privacy, Data mining
CITATION
Balamurugan Anandan, Chris Clifton, "Significance of Term Relationships on Anonymization", WI-IAT, 2011, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies 2011, pp. 253-256, doi:10.1109/WI-IAT.2011.240
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