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 by storing and analyzing past searches submitted by them. For improving better search quality the String Similarity Match Algorithm (SSM Algorithm) can be implemented with the proposed. Current solutions propose new mechanisms that introduce a high cost in terms of computation and communication, to address this privacy threat. Personalized search is a promising way to improve the accuracy of web search, also it is attracting much attention recently. Effective personalized search requires collecting and aggregating user information, which often raises serious concerns of privacy infringement for many users. These concerns have become one of the main barriers for deploying personalized search applications, and privacy-preserving personalization is a great challenge. Adversaries are tried to resist in proposed system with the help of broader background knowledge (i. e. 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. With the help of this mechanism, privacy can be achieved.