IEEE 2014 JAVA NETWORK SECURITY PROJECTS Supporting privacy protection in personalized web search

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The document discusses supporting privacy protection in personalized web search. It proposes a framework called UPS that can generalize user profiles for each query based on user-specified privacy requirements to balance personalization utility and privacy risk. Two algorithms, GreedyDP and GreedyIL, are developed for runtime profile generalization, with GreedyIL significantly outperforming GreedyDP in efficiency. Extensive experiments demonstrate the effectiveness of the UPS framework in improving search quality while preserving user privacy.

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This document proposes a personalized web search (PWS) framework called UPS that protects user privacy during search. UPS can generalize user profiles to different levels based on privacy requirements while balancing personalization utility and privacy risk. It presents two greedy algorithms, GreedyDP and GreedyIL, for runtime profile generalization and an online mechanism for deciding when to personalize queries. Experiments show UPS effectively protects privacy while maintaining personalization benefits and GreedyIL outperforms GreedyDP in efficiency.

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The document proposes a privacy-preserving personalized web search framework called UPS. UPS can generalize user profiles for each query according to user-specified privacy requirements. It uses two metrics to evaluate privacy breach risk and query utility for hierarchical user profiles. The framework includes two algorithms for generalizing user profiles at runtime. It also provides an online mechanism for deciding whether to personalize queries to enhance search quality while avoiding unnecessary privacy exposures. Extensive experiments showed the proposed approach efficiently protects privacy during personalized web searches.

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The document proposes a privacy-preserving personalized web search framework called UPS. It aims to generalize user profiles for each query according to user-specified privacy requirements, while balancing personalization utility and privacy risk. Two algorithms, GreedyDP and GreedyIL, are developed to support runtime profile generalization. An online mechanism is also provided to decide whether personalizing a query would be beneficial without compromising privacy. Experiments show the effectiveness and efficiency of the UPS framework in achieving personalized search results while preserving user privacy.

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This document summarizes a research paper about supporting privacy protection in personalized web search. The researchers propose a privacy-preserving personalized web search framework that can dynamically generate user profiles for queries while respecting user privacy preferences. Two greedy algorithms, Greedy1 and Greedy2, are developed for runtime profile inference to balance customization and privacy risks. Large-scale experiments show that Greedy2 significantly outperforms Greedy1 in terms of efficiency. The results indicate that the proposed framework can achieve good search results while maintaining user privacy.

Supporting privacy protection in personalized web search

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This document proposes a framework called UPS that aims to balance privacy and personalization in personalized web search. UPS generalizes user profiles for each query based on the user's specified privacy requirements. It has two phases - an offline phase to construct profiles and an online phase to handle queries. When a query is issued, UPS generates a generalized user profile considering metrics for personalization utility and privacy risk. The profile and query are then sent to search, with results personalized and delivered back. This addresses issues with existing systems by supporting runtime profiling while respecting privacy.

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This document summarizes a project that proposes a privacy-preserving personalized web search framework called UPS. UPS allows users to specify customized privacy requirements for their hierarchical user profiles. It performs online generalization of user profiles for each query to balance personalization utility and privacy risk without compromising search quality or unnecessarily exposing user profiles. The framework consists of client and server components, with the client maintaining profiles and privacy specifications and handling query processing and result personalization.

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