Abstract-Web query log data contain information useful to research; however, release of such data can re-identify the search engine users issuing the queries. These privacy concerns go far beyond removing explicitly identifying information such as name and address, since non-identifying personal data can be combined with publicly available information to pinpoint to an individual. In this work we model web query logs as unstructured transaction data and present a novel transaction anonymization technique based on clustering and generalization techniques to achieve the k-anonymity privacy. We conduct extensive experiments on the AOL query log data. Our results show that this method results in a higher data utility compared to the stateof-the-art transaction anonymization methods.
In a similar approach, the authors in REF adopted the k-anonymity privacy constraint for a transactional dataset.
An Effective Clustering Approach to Web Query Log Anonymization
Proc. 2010 International Conference on Security and Cryptography (SECRYPT'10), July 2010, Athens, Greece
Computer Science