Date
2017
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.
Supervisor
Description
peer-reviewed
Publisher
Association for Computing Machinery
Citation
ASE 2017 Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering;pp. 280-285
Collections
Lero - The Science Foundation Ireland Research Centre for Software
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Nuseibeh_2017_Learning.pdf
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Keywords
on-line social networks, privacy control, Facebook
ULRR Identifiers
https://hdl.handle.net/10344/6787
 https://doi.org/10.34961/525
Funding code
Funding Information
EPSRC, European Research Council (ERC), Science Foundation Ireland (SFI)
Sustainable Development Goals
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