User Customized Privacy Protection in Personalized Web Search

Abstract

Personalized web search has been introduced to enhance the user experience in faster decision making by neglecting the least relevant web search results for user. At the same time users do not want their personal information to be revealed to the outside world. User’s disinclination to tell their personal information during search has becomes a major barricade for the wide build-up of personalized web search. Achieving the greater privacy along with the personalization is big challenge where previous researches could not able to achieve to the complete extent. This paper discusses privacy protection in personalized web search applications that represents userdesire as taxonomy user profiles. Generalize profile by queries while reference user specified a private requirementusing a personalized web search framework called User Customizable Privacy Preserving Search (ups). The UPS framework is a for step process. They are generating the user profile, privacy requirement customization, mapping the query topic with the corresponding domain and runtime profile generalization. And also in this paper we study how the two predictive metrics personalized and privacy protection is achieved with the help of two algorithms namely Greedy Discriminating Power and Greedy Information Loss algorithms respectively.
Key Words: Privacy protection; Profile generalization; privacy requirement customization; personalized search; privacy risk; Search engines.