A General Algorithm for k-anonymity on Dynamic Databases
Julián Salas, Vicenç Torra · Lecture notes in computer science · 2018
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.