Everything you control is not everything: Achieving intention-concealed visit on social networks. Issue 119 (August 2022) - Li, Helin; Zhu, Hui; Lin, Xiaodong; Lu, Rongxing; Yu, Zhipeng; Lan, Wei
Abstract: 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 ntention-c 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 and picked decoy visits. Aviv is implemented as a credible private third-party and tested on a real OSN, the performance evaluation results show that Aviv is effective and efficient.