Layered Approach for Personalized Search Engine Logs Privacy Preserving

In this paper we examine the problem of defending privacy for publishing search engine logs. Search engines play a vital role in the navigation through the enormity of the Web. Privacy-preserving data publishing (PPDP) provides techniques and tools for publishing helpful information while preserving data privacy. Recently, PPDP has received significant attention in research communities, and several approaches have been proposed for different data publishing situations. In this paper we learn privacy preservation for the publication of search engine query logs. Consider a subject that even after eliminating all personal characteristics of the searcher, which can serve as associations to his identity, the magazine of such data, is still subject to privacy attacks from opponents who have partial knowledge about the set. Our tentative results show that the query log can be appropriately anonymized against the particular attack, while retaining a significant volume of helpful data. In this paper we learn about problem in search logs and why the log is not secure and how to create log secure using data mining algorithm and methods like Generalization, Suppression and Quasi identifier.