ASE 2017

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2017), October 30 – November 3, 2017, Urbana-Champaign, IL, USA

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Technical Research
Learning to Share: Engineering Adaptive Decision-Support for Online Social Networks
Yasmin Rafiq, Luke Dickens, Alessandra Russo, Arosha K. Bandara, Mu Yang, Avelie Stuart, Mark Levine, Gul Calikli, Blaine A. Price, and Bashar Nuseibeh
(Imperial College London, UK; University College London, UK; Open University, UK; University of Southampton, UK; University of Exeter, UK; Chalmers University of Technology, Sweden; University of Gothenburg, Sweden; Lero, Ireland)
Abstract: Some online social networks (OSNs) allow users to define $emph{friendship-groups}$ 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 $emph{cross-posting}$ poses privacy risks. This paper presents a $emph{learning to share}$ 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.


Time stamp: 2019-06-18T17:57:38+02:00