Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on (2011)
Aug. 22, 2011 to Aug. 27, 2011
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.