Ppdp-mlt: K−anonymity Privacy Preservation for Publishing Search Engine Logs

In this paper we investigate the problem of protecting privacy for publishing search engine logs. Search engines play a crucial role in the navigation through the vastness of the Web. Privacy-preserving data publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Recently, PPDP has received considerable attention in research communities, and many approaches have been proposed for different data publishing scenarios. In this paper we study privacy preservation for the publication of search engine query logs. Consider an issue that even after removing all personal characteristics of the searcher, which can serve as links to his identity, the publication of such data, is still subject to privacy attacks from adversaries who have partial knowledge about the set. Our experimental results show that the query log can be appropriately anonymized against the specific attack, while retaining a significant volume of useful data. In this paper we study about problem in search logs and why the log is not secure and how to make log secure using data mining algorithm and techniques like Generalization, Suppression and Quasi identifier.