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Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on (2011)
Lyon, France
Aug. 22, 2011 to Aug. 27, 2011
ISBN: 978-0-7695-4513-4
pp: 253-256
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.
Privacy, Data mining

B. Anandan and C. Clifton, "Significance of Term Relationships on Anonymization," 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies(WI-IAT), Lyon, 2011, pp. 253-256.
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