Seminar Future Internet SS2013

Anonymization of published microdata has become a very im-portant topic nowadays. The major difficulty is to publish data of individuals in a manner that the released table both provides enough information to the public and prevents dis-closure of sensitive information. Therefore, several authors proposed definitions of privacy to get anonymous microdata. One definition is called k-Anonymity and states that every individual in one generalized block is indistinguishable from at least k- 1 other individuals. `-Diversity uses a stronger privacy definition and claims that every generalized block has to contain at least ` different sensitive values. Another defi-nition is called t-Closeness. It demands that the distribution of one sensitive value of a generalized block is close to its distribution in the entire table. This paper mainly deals with the principle and notion of `-Diversity. Therefore, two methods called Homogeneity and Background-Knowledge Attack are discussed to break the pri-vacy constraints of k-Anonymity. Then a model to reason about privacy in microdata, namely Bayes-Optimal Privacy, is introduced. Based on k-Anonymity and Bayes-Optimal Privacy the principle and several instantiations of `-Diver-sity are discussed. At the end `-Diversity is applied to a real database gathered from several Android devices