Privacy Protection Using CPS Framework in Personalized Web Search
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 11
Abstract
Personalized web search (PWS) is efficient in improving the search quality on internet. But users generally hesitate to disclose their personal information during search which has become a hindrance in wide increase in PWS. We model user preferences into user profiles. We proposed a PWS framework called CPS while performs profile generalization without affecting users' privacy requirements. This generalization maintains a balance between personalization and privacy risk while a generalized profile is exposed. We propose two algorithms namely GreedyDP and GreedyIL helping us in runtime generalization. We provide an online prediction mechanism to decide whether personalizing a query is helpful.
Authors and Affiliations
Priyanka Zaware, Satish R Todmal
Keywords
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- EP ID EP19051
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