Genetic Algorithm for Privacy Protected Personalized Web Search

The search engine becomes the most important gateway for ordinary people who are looking for useful information on the web. In spite of, users might sense failure when search engines return incorrect results that do not meet their real objectives. Such inappropriateness is due in large part to the variety of users' contexts and backgrounds, as well as the uncertainty of texts. Personalized web search (PWS) is a type of search techniques which aims at providing better search results, which restricts to individual user needs. In this paper, UPS (User customizable Privacy- preserving Search) framework is proposed which generalize user profiles for each query given to user-specified privacy specification. This generalization has aims of keeping a balance between two predictive metrics that evaluate the utility of personalization and the privacy risk of exposing the user generalized profile. When considering bi-objectives problem through greedy search, it does not adequately handle the objectives dynamically or adaptively. So, to handle these goals genetic algorithm developed in this paper. The derived results will be compared based on precision and time.