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Everything you control is not everything: Achieving intention-concealed visit on social networks by Helin Li; Hui Zhu; Xiaodong Lin; Rongxing Lu; Zhipeng Yu; Wei Lan is a Computer Science article available to read on EtoBox.

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With the flourishment of Artificial Intelligence (AI), the quality of services on online social networks (OSNs) has improved tremendously. Through the introduction of AI into OSNs, providers enhance the users experience. However, the risk of data misuse has also been magnified. And there has been a trend towards users' mistrust when it comes to sharing their true intention on OSN platforms, primarily because of privacy concerns. In this paper, we propose an effective intention-concealing visitation framework named Aviv, which acts as a credible and private third-party where it generates a rtificial i ntentionc oncealed b rowsing (AICB trajectories) for users to conceal their true intentions. The browsing trajectory is a sequence composed of blogs, bloggers, news, or a mixture containing visitable content. Specifically, Aviv first extracts accessible graphs composed of visitable contents from applied OSN platforms. Then an elaborate and personalized divert scoring process is conducted to measure optional decoy visits' effectiveness. Finally, using the divert scoring values, elaborate and personalized decoy visits are picked out, Aviv composes AICB trajectories with true intentions a

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Author

Helin Li; Hui Zhu; Xiaodong Lin; Rongxing Lu; Zhipeng Yu; Wei Lan

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)