SECURED AN IMPLEMENTATION OF PERSONALIZED WEB SEARCH

Abstract

Personalized web search is one of the growing concepts in the web technologies. Personalization of web search is to carry out retrieval for each user incorporating his/her interests. For a given query, a personalized Web search can provide different search results for different users or organize search results differently for each user, based upon their interests, preferences, and information needs. There are many personalized web search algorithms for analyzing the user interests and producing the outcome quickly; Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. However, display show that users’ reluctance to disclose their private information during search has become a major barrier for the wide proliferation of SPWS. We study privacy protection in SPWS applications that model user preferences as hierarchical user profiles. We propose a SPWS framework called UPS that can adaptively generalize profiles by queries while respecting user specified privacy exaction. Our runtime generalization aims at striking a balance between two predictive metrics that evaluate the utility of personalization and the privacy risk of exposing the abstraction profile. We present Greedy Algorithm and Rating algorithm, for runtime generalization. We also provide an online prediction mechanism for deciding whether personalizing a query is beneficial. Extensive experiments exhibition the effectiveness of our framework. The empirical results also reveal that Symmetric key and new Advanced Encryption Standard (AES) in terms of efficiency.