Utility preserving query log anonymization via semantic microaggregation

•A utility preserving query log anonymization method is presented.•Data semantics are considering during the whole anonymization process.•The MDAV microaggregation method is adapted to support textual set-valued data.•An evaluation using query logs from AOL and ODP as knowledge base has been performed.•Results show that our approach preserves query logs utility better than related works.

Query logs are of great interest for scientists and companies for research, statistical and commercial purposes. However, the availability of query logs for secondary uses raises privacy issues since they allow the identification and/or revelation of sensitive information about individual users. Hence, query anonymization is crucial to avoid identity disclosure. To enable the publication of privacy-preserved – but still useful – query logs, in this paper, we present an anonymization method based on semantic microaggregation. Our proposal aims at minimizing the disclosure risk of anonymized query logs while retaining their semantics as much as possible. First, a method to map queries to their formal semantics extracted from the structured categories of the Open Directory Project is presented. Then, a microaggregation method is adapted to perform a semantically-grounded anonymization of query logs. To do so, appropriate semantic similarity and semantic aggregation functions are proposed. Experiments performed using real AOL query logs show that our proposal better retains the utility of anonymized query logs than other related works, while also minimizing the disclosure risk.