期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2015
卷号：6
期号：4
页码：3600-3603
出版社：TechScience Publications
摘要：Personalized web search (PWS) has gained its efficiency in increasing the quality of a variety of search services on the Internet. On the other hand, indication shows that users’ unwillingness to reveal their private data during search has become an important barrier for the wide creation profiles of personalized web search. This paper proposes a PWS framework called User Personalized Search (UPS) that can concurrently generalize profiles by user queries while maintaining user specified privacy conditions. This paper focuses on runtime generalization aims at remarkable balance between two effective metrics that generate the utility of privacy and personalization risk of exposing the user generalized profile. This paper presents two greedy algorithms, namely GreedyIL, GreedyDP and for runtime generalization. Further this paper utilizes the Useless User Profile (UUP) to reduce the number of collaborations with the server which in turn reduces the time complexity
