Analyzing mechanism-based attacks in privacy-preserving data publishing

Privacy-preserving data publishing (PPDP) deals with data publishing while preserving individual's privacy in the data. Recent researches show that knowledge of mechanism of anonymization provides a loophole for attacks [1] and [2], we call this kind of attack a mechanism-based attack. In this paper, we first give a comprehensive study of mechanism-based attack and point out that the range of mechanism-based disclosure is much broader than ever said. Then, we analyze the rationale of mechanism-based attack and give the definition of mechanism-based attack formally. To counteract mechanism-based attack, we introduce a model called e-secrecy and corresponding solution algorithm MAIA. We conduct a comprehensive set of experiments to show mechanism-based attacks are practical concern in the real-world data sets and that our method introduces better data utility and very minor computation than the existing algorithms.