Web query log data contain information useful to research; however, releaseof such data can re-identify the search engine users issuing the queries. Theseprivacy concerns go far beyond removing explicitly identifying information suchas name and address, since non-identifying personal data can be combined withpublicly available information to pinpoint to an individual. In this work wemodel web query logs as unstructured transaction data and present a noveltransaction anonymization technique based on clustering and generalizationtechniques to achieve the k-anonymity privacy. We conduct extensive experimentson the AOL query log data. Our results show that this method results in ahigher data utility compared to the state of-the-art transaction anonymizationmethods.
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