  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 state of-the-art transaction anonymization
methods.
