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

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conference contribution
posted on 2018-04-26, 09:04 authored by Yasmin Rafiq, Luke Dickens, Alessandra Russo, Arosha K. Bandara, Mu Yang, Avelie Stuart, Mark Levine, Gul Calikli, Blaine A. Price, Bashar NuseibehBashar Nuseibeh
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.

Funding

Earthquake Damageability of Low-Rise Construction

Directorate for Engineering

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Study on Aerodynamic Characteristics Control of Slender Body Using Active Flow Control Technique

Japan Society for the Promotion of Science

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History

Publication

ASE 2017 Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering;pp. 280-285

Publisher

Assocation for Computing Machinery

Note

peer-reviewed

Other Funding information

EPSRC, ERC, SFI

Rights

"© ACM, YYYY. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published inASE 2017 Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering, pp. 280-285, https://dl.acm.org/citation.cfm?id=3155600

Language

English

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