scholarly journals Intent-aware Query Obfuscation for Privacy Protection in Personalized Web Search

2018 ◽  
Author(s):  
Wasi Uddin Ahmad ◽  
Kai-Wei Chang ◽  
Hongning Wang
Keyword(s):  
Privacy Protection ◽  
Web Search ◽  
Query Obfuscation

Web search query privacy: Evaluating query obfuscation and anonymizing networks1

2014 ◽  
Vol 22 (1) ◽  
pp. 155-199 ◽  
Author(s):  
Sai Teja Peddinti ◽  
Nitesh Saxena
Keyword(s):  
Web Search ◽  
Search Query ◽  
Query Privacy ◽  
Query Obfuscation

scholarly journals Client Side Privacy Protection Using Personalized Web Search

2016 ◽  
Vol 79 ◽  
pp. 1029-1035 ◽  
Author(s):  
Sharvari V. Malthankar ◽  
Shilpa Kolte
Keyword(s):  
Privacy Protection ◽  
Web Search ◽  
Client Side

scholarly journals Designing Improvisation in Privacy Protection for Personalized Web Search using deep learning approach

2021 ◽  
Vol 10 (6) ◽  
pp. 25336-25346
Author(s):  
Ms. Sushama K Bhandare ◽  
Dr. Avinash S. Kapse
Keyword(s):  
Private Information ◽  
Privacy Protection ◽  
Web Search ◽  
Greedy Algorithms ◽  
User Preferences ◽  
Privacy Risk ◽  
Major Barrier ◽  
Index Terms ◽  
Prediction Mechanism

Abstract—Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. However, evidences show that users’ reluctance to disclose their private information during search has become a major barrier for the wide proliferation of PWS. We study privacy protection in PWS applications that model user preferences as hierarchical user profiles. We propose a PWS framework called UPS that can adaptively generalize profiles by queries while respecting user- specified privacy requirements. 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 generalized profile. We present two greedy algorithms, namely GreedyDP and GreedyIL, for runtime generalization. We also provide an online prediction mechanism for deciding whether personalizing a query is beneficial. Extensive experiments demonstrate the effectiveness of our framework. The experimental results also reveal that GreedyIL significantly outperforms GreedyDP in terms of efficiency.   Index Terms—Privacy protection, personalized web search, utility, risk, profile

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