An Effective Clustering Approach to Web Query Log Anonymization

Details An Effective Clustering Approach to Web Query Log Anonymization An Effective Clustering Approach to Web Query Log Anonymization

2010-12-03

arxiv.org/abs/1012.0663v1

Proc. 2010 International Conference on Security and Cryptography (SECRYPT'10), July 2010, Athens, Greece

Computer Science

Databases

9 pages

Scientific paper

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.

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