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    Learning to share: engineering adaptive decision-support for online social networks

    Rafiq, Y. and Dickens, L. and Russo, A. and Bandara, A.K. and Yang, Mu and Stuart, A. and Levine, M. and Calikli, G. and Price, B.A. and Nuseibeh, B. (2017) Learning to share: engineering adaptive decision-support for online social networks. In: 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), 30 Oct - 03 Nov 2017, Urbana-Champaign, U.S..

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    Abstract

    Some online social networks (OSNs) allow users to define friendship-groups as reusable shortcuts for sharing information with multiple contacts. Posting exclusively to a friendship-group gives some privacy control, while supporting communication with (and within) this group. However, recipients of such posts may want to reuse content for their own social advantage, and can bypass existing controls by copy-pasting into a new post; this cross-posting poses privacy risks. This paper presents a learning to share approach that enables the incorporation of more nuanced privacy controls into OSNs. Specifically, we propose a reusable, adaptive software architecture that uses rigorous runtime analysis to help OSN users to make informed decisions about suitable audiences for their posts. This is achieved by supporting dynamic formation of recipient-groups that benefit social interactions while reducing privacy risks. We exemplify the use of our approach in the context of Facebook.

    Metadata

    Item Type: Conference or Workshop Item (Paper)
    School: School of Business, Economics & Informatics > Management
    Depositing User: Dr Mu Yang
    Date Deposited: 04 Oct 2022 13:22
    Last Modified: 04 Oct 2022 13:22
    URI: https://eprints.bbk.ac.uk/id/eprint/49062

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