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Supporting Privacy Protection in Personalized Web Search


A. Arun , Valliammai Engineering College; S. Suma, Valliammai Engineering College


Privacy Protection, Web Search


Personalized web search (PWS) has demonstrated that it is effective in improving the quality of various search services on the Internet. However, evidences show that users’ reluctance to discover their private information during search has become a major revetment for the wide proliferation of PWS. We study privacy protection in PWS applications that model user preferences as stratified user profiles. We propose a PWS framework called UPS that can adaptative generalize profiles by queries while respecting user-specified privacy requirements. Our runtime generalization aims at striking a balance between two prophetical metrics that valuate the utility of reification and the privacy risk of exposing the generalized profile. We present two greedy algorithms, namely GreedyDP and GreedyIL, for runtime induction. We also provide an online prevision mechanism for deciding whether personalizing a query is beneficial. Extensive experiments demonstrate the effectiveness of our framework. The simulation results also reveal that GreedyIL significantly outperforms GreedyDP in terms of efficiency.

Other Details

Paper ID: IJSRDV5I10047
Published in: Volume : 5, Issue : 1
Publication Date: 01/04/2017
Page(s): 36-39

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