Disclosure Control by Computer Scientists: An Overview and an Application of Microaggregation to Mobility Data Anonymization

Privacy-preserving data mining (PPDM) is a subdiscipline of computer science which in many respects is parallel to statistical disclosure control (SDC) within statistics. See [12] for a survey of recent developments in PPDM. We focus here on the connections between k-anonymity, a concept arisen in the PPDM community, and microaggregation, a family of methods developed within SDC. This is discussed at a conceptual level in Section 2. We then move to anonymization of mobility data, i.e. trajectories, a very dynamic area in PPDM and a completely neglected one in SDC. In Section 3 we apply the microaggregation approach to k-anonymize real-world trajectories. We present a new distance measure for spatio-temporal data that facilitates the microaggregation process. The measure naturally considers both spatial and temporal aspects and can be fine-tuned for specific applications and instantiated with existing measures for spatial data, sequences, or time series. Conclusions are summarized in Section 4.