Hiding distinguished ones into crowd: privacy-preserving publishing data with outliers

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, we study the problem of anonymizing the micro-data that contains outliers. We 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. To defend against the distinguishability-based attack, we define the plain k-anonymity as the privacy principle. Based on the definition, we categorize the outliers into two types, the ones that cannot be hidden by any plain k-anonymous group (called global outliers) and the ones that can (called local outliers). We propose the algorithm to efficiently anonymize local outliers with low information loss. Our experiments demonstrate the efficiency and effectiveness of our approach.