2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) (2017)
Urbana, IL, USA
Oct. 30, 2017 to Nov. 3, 2017
Yasmin Rafiq , Imperial College London UK
Luke Dickens , University College London, UK
Alessandra Russo , Imperial College London UK
Arosha K. Bandara , The Open University, UK
Mu Yang , University of Southampton, UK
Avelie Stuart , University of Exeter, UK
Mark Levine , University of Exeter, UK
Gul Calikli , Chalmers & University of Gothenburg, Sweden
Blaine A. Price , The Open University, UK
Bashar Nuseibeh , The Open University, UK
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.
Privacy, Facebook, Monitoring, Sensitivity, Computational modeling, Adaptation models
Y. Rafiq et al., "Learning to share: Engineering adaptive decision-support for online social networks," 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), Urbana, IL, USA, 2017, pp. 280-285.