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Publishing microdata raises concerns of individual privacy. When there exist outlier records in the microdata, the distinguishability of the outliers enables their privacy to be easier to be compromised than that of regular ones. However, none of the existing anonymization techniques can provide sufficient protection to the privacy of the outliers. In this paper, the authors study the problem of anonymizing the microdata that contains outliers. They define the distinguishability based attack by which the adversary can infer the existence of outliers as well as their private information from the anonymized microdata.
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