User k-anonymity for privacy preserving data mining of query logs

The anonymization of query logs is an important process that needs to be performed prior to the publication of such sensitive data. This ensures the anonymity of the users in the logs, a problem that has been already found in released logs from well known companies. This paper presents the anonymization of query logs using microaggregation. Our proposal ensures the k-anonymity of the users in the query log, while preserving its utility. We provide the evaluation of our proposal in real query logs, showing the privacy and utility achieved, as well as providing estimations for the use of such data in data mining processes based on clustering.

Research highlights
► We introduce microaggregation to anonymize query logs, providing user k-anonymity.
► Our method can preserve most of the data utility and user profile, while providing enough anonymity to the users.
► Utility is also considered for clustering-based data mining processes.