Title :
An effective clustering approach to web query log anonymization
Author :
Fard, Amin Milani ; Wang, Ke
Author_Institution :
School of Computing Science, Simon Fraser University, BC, V5A 1S6, Burnaby, Canada
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 state-of-the-art transaction anonymization methods.
Keywords :
Clustering algorithms; Dairy products; Data privacy; Helium; Silicon; Taxonomy; Transaction databases; Item Generalization; Privacy-preserving Data Publishing; Query Logs Data; Transaction Data Anonymization;
Conference_Titel :
Security and Cryptography (SECRYPT), Proceedings of the 2010 International Conference on
Conference_Location :
Athens