Sustaining Isolation Security for Preserving User¬ís Seclusion in Web Search Engines
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We apply a PWS support called UPS that can adaptively simplify profiles by queries as regarding user specific privacy necessities. Our runtime simplification aims at conspicuous a equilibrium among two analytical metrics that estimate the service of personalization and the isolation danger of revealing the general outline. We present two greedy algorithms, specifically Greedy DP and Greedy IL, for runtime simplification. We also present an online prediction machinery for deciding whether personalizing a query is useful. Wide-ranging experiments demonstrate the effectiveness of our framework. The experimental results also expose that Greedy IL significantly outperforms Greedy DP in terms of efficiency. In this paper we present a novel procedure specifically calculated to protect the users’ privacy in front of web search profiling. Our system provides a distorted user profile to the web search engine. We offer implementation details and computational and communication results that show that the proposed protocol improves the existing solutions in terms of query delay. Our scheme provides an reasonable overhead while contribution seclusion benefits to the users.