Learning to Share: Engineering Adaptive Decision-Support for Online Social Networks
Institute of Electrical and Electronics Engineers (IEEE) / Association for Computing Machinery (ACM)
Reason for embargo
Currently under an indefinite embargo pending publication by IEEE. Add DOI and remover embargo on publication
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.
We would like to thank EPSRC, SFI and the ERC for their financial support.
This is the author accepted manuscript.
2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2017), 30 October - 3 November 2017, Urbana-Champaign, Illinois, USA