A General Algorithm for k-anonymity on Dynamic Databases

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.CC BY 4.0Also part of the Security and Cryptology book sub series (LNSC, volume 11025)Julián Salas acknowledges the support of a UOC postdoctoral fellowship. This work is partly funded by the Spanish Government through grant TIN2014-57364-C2-2-R “SMARTGLACIS”. Vicenç Torra acknowledges the suport of Vetenskapsrdet project: “Disclosure risk and transparency in big data privacy” (VR 2016-03346, 2017-2020).DRIAT