A General Algorithm for k-anonymity on Dynamic Databases

Abstract

In this work we present an algorithm for k-anonymization of datasets that are changing over time. It is intended for preventing identity disclosure in dynamic datasets via microaggregation. It supports adding, deleting and updating records in a database, while keeping k-anonymity on each release. We carry out experiments on database anonymization. We expected that the additional constraints for k-anonymization of dynamic databases would entail a larger information loss, however it stays close to MDAV’s information loss for static databases. Finally, we carry out a proof of concept experiment with directed degree sequence anonymization, in which the removal or addition of records, implies the modification of other records.