The slides above are from a presentation of our work on learning privacy norms for social software at the Symposium on Engineering Adaptive and Self-Managing Systems (SEAMS) in Austin, Texas. The paper describes an architecture for integrating privacy management capabilities into social applications that integrate sharing functionality using social media platforms like Facebook. The following summary is extracted from the abstract of the paper that accompanies this presentation:
Privacy Dynamics, is an adaptive architecture that learns privacy norms for different audience groups based on users’ sharing behaviours. Our architecture is underpinned by a formal model inspired by social identity theory, a social psychology framework for analysing group processes and intergroup relations. Our formal model comprises two main concepts, the group membership as a Social Identity (SI) map and privacy norms as a set of conflict rules. In our approach a privacy norm is specified in terms of the information objects that should be prevented from flowing between two conflicting social identity groups.
Some interesting questions and observations resulting from the presentation included:
- Using abstractions of object attributes to manage the combinatorial explosion caused by having to represent lots of attributes in order to learn rules that differentiate sharing behaviours.
- Considering different ways of extracting the relevant attributes associated with the different sharing behaviours.
- Looking at the interactions between users on the social media platform to guide the specification of social identity groups.
Calikli, Gul; Law, Mark; Bandara, Arosha K.; Russo, Alesandra; Dickens, Luke; Price, Blaine A.; Stuart, Avelie; Levine, Mark and Nuseibeh, Bashar (2016). Privacy Dynamics: Learning Privacy Norms for Social Software. In: 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, 16-17 May 2016, Austin, Texas, USA, Association of Computing Machinery