A Novel Approach for Improved Personalized Web Search with Privacy Protection
R. Ravikumar1, U. Sundhar2
1R. Ravikumar, M.E-Scholar, Department of CSE, Thiruvalluvar College of Engineering & Technology, (Tamil Nadu), India.
2U. Sundhar, Asst. Prof., Department of CSE, Thiruvalluvar College of Engineering & Technology, (Tamil Nadu), India.
Manuscript received on 23 May 2015 | Revised Manuscript received on 30 May 2015 | Manuscript published on 30 May 2015 | PP: 11-14 | Volume-4 Issue-2, May 2015 | Retrieval Number: A1333034115©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Personalized web search is a promising way to improve search quality by customizing search results for people with individual information goals. However, users are uncomfortable with exposing private information to search engines. Thus, a balance must be struck between search quality and privacy protection. The proposed system presents a scalable way for users to automatically build rich user profiles. These profiles summarize user’s interests into a hierarchical organization according to specific interests. The system proposes a Personalized Web (PWS) framework called User customizable Privacy-preserving Search (UPS) that can adaptively generalize profiles by queries while respecting user specified privacy requirements. The 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. The system presents two greedy algorithms, namely Greedy DP and Greedy IL, for runtime generalization. It also provides an online prediction mechanism for deciding whether personalizing a query is beneficial.
Keywords: Privacy-preserving Search, Privacy-preserving Search, Greedy DP and Greedy IL
Scope of the Article: Security, Privacy and Trust in IoT & IoE