Differentially Private K-Anonymity

Research in privacy preserving data publication can be broadly categorized in two classes. Syntactic privacy definitions have been under the cursor of the research community for the past many years. A lot of research is primarily dedicated to developing algorithms and notions for syntactic privacy that thwart the re-identification attacks. Sweeney and Samarati proposed a well-known syntactic privacy definition coined K-anonymity for thwarting linking attacks using quasi-identifiers. Thanks to its conceptual simplicity, K-anonymity has been widely implemented as a practicable definition of syntactic privacy, and owing to algorithmic advancement for K-anonymous versions of micro-data, K-anonymity has attained much anticipated popularity. Semantic privacy definitions do not take into account the adversarial background knowledge but rather forces the sanitization algorithms (mechanisms) to satisfy a strong semantic property by the way of random processes. Though semantic privacy definitions are theoretically immune to any kind of adversarial attacks, their applicability in real-life scenarios has come under criticism. In order to make the semantic definitions more practical, the research community has focused its attention towards combining the practicalness of syntactic privacy with the strength of semantic approaches [7] such that we may in the near future benefit from both research tracks.