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2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
Significance of Term Relationships on Anonymization
Lyon, France
August 22-August 27
ISBN: 978-0-7695-4513-4
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, vol. 3, pp.253-256, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011
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