k-anonymity: Risks and the Reality

Abstract

Many a time, datasets containing private and sensitive information are useful for third-party data mining. To prevent identification of personal information, data owners release such data using privacy-preserving data publishing techniques. One well-known technique - k-anonymity - proposes that the records be grouped based on quasi-identifiers such that quasi-identifiers in a group have exactly the same values as any other in the same group. This process reduces the worst-case probability of re-identification of the records based on the quasi identifiers to 1/k. The problem of optimal k-anonymisation is NP-hard. Depending on the k-anonymisation method used and the number of quasi identifiers known to the attacker, the probability of re-identification could be lower than the worst-case guarantee. We quantify risk as the probability of re-identification and propose a mechanism to compute the empirical risk with respect to the cost of acquiring the knowledge about quasi-identifiers, using an real-world dataset released with some k-anonymity guarantee. In addition, we show that k-anonymity can be harmful because the knowledge of additional attributes other than quasi-identifiers can raise the probability of re-identification.