Supporting Privacy Protection in Personalized Web Search with Secured User Profile

Web search engines (e. g. Google, Yahoo, Microsoft Live Search, etc. ) are widely used to nd certain data among a huge amount of information in a minimal amount of time. These useful tools also pose a privacy threat to the users. Web search engines prole their users on the basis of past searches submitted by them. In the proposed system, we can implement the String Similarity Match Algorithm (SSM Algorithm) for improving the better search quality results. To address this privacy threat, current solutions propose new mechanisms that introduce a high cost in terms of computation and communication. Personalized search is promising way to improve the accuracy of web search. However, effective personalized search requires collecting and aggregating user information, which often raises serious concerns of privacy infringement for many users. Indeed, these concerns have become one of the main barriers for deploying personalized search applications, and how to do privacy-preserving personalization is a great challenge. In this we propose and try to resist adversaries with broader background knowledge, such as richer relationship among topics. Richer relationship means we generalize the user profile results by using the background knowledge which is going to store in history. Through this we can hide the user search results. By using this mechanism, we can achieve the privacy