Generically extending anonymization algorithms to deal with successive queries
Proceedings of the 21st ACM international conference on Information and knowledge management
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Privacy-Preserving Data Re-publishing (PPDR) deals with publishing microdata in dynamic scenarios. Due to privacy concerns, data must be disguised before being published. Research in privacy-preserving data publishing (PPDP) has proposed many such methods on static data. In PPDR, multiple appeared records can be used to infer private information of other records. Therefore, inference channels exist among different releases. To understand the privacy property of data re-publishing, we need to analyze the impact of these inference channels. Previous studies show such analysis when data are updated or disguised in special ways, however, no general method has been proposed. Using the Maximum Entropy Modeling method, we have developed a general solution. Our method can conduct inference analysis when data are arbitrarily updated or arbitrarily disguised using either generalization or bucketization, two most common data disguise methods in PPDR. Through analysis and experiments, we demonstrate the advantage and the effectiveness of our method.