Everything you control is not everything: Achieving intention-concealed visit on social networks

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 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.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

With the application of artificial intelligence in online social networks, the user experience has been improved, but the risk of data misuse has also increased. In order to address the issue of users' reluctance to share their true intentions due to privacy concerns, this paper proposes an effective intention-concealing visitation framework named Aviv.