Software Engineering for Usable Mobile Privacy Management
The overall aim of the this
Microsoft funded SEIF project
is to provide software engineers with tools to understand privacy requirements
for mobile applications and to help engineer these into applications that may
evolve at design time and will certainly adapt at runtime.
Our vision is to provide software developers tools to help them understand and
support the users of their mobile applications. A key innovation of these tools
will be support for provision and maintenance of links between what users, or
privacy policies, say about privacy and what the software actually does.
The project delivered a tool-supported argumentation language to express and
reason about dynamic and personal privacy requirements of mobile users. The
software engineering framework provided a synchronous support for the
traceability between privacy requirements and mobile code to control the
disclosure of information to the right people under meaningful contexts. A
simulation of varying awareness settings of persona's helps software
engineers to gain a better understanding of privacy requirements in the social
| Bashar Nuseibeh
|| Arosha Bandara
|| Blaine Price
|| Yijun Yu
|| Thein Than Tun
|| Charles B. Haley
|| Inah Omoronyia
security & privacy arguments
Tun, Thein Than; Bandara, Arosha K.; Price, Blaine A.; Yu, Yijun; Haley, Charles; Omoronyia, Inah and Nuseibeh, Bashar (2012). Privacy arguments: analysing selective disclosure requirements for mobile applications. In: 20th IEEE International Requirements Engineering Conference, 24-28 September 2012 , Chicago, Illinois.
Yu, Yijun; Lin, Yu; Hu, Zhenjiang; Hidaka, Soichiro; Hiroyuki, Kato and
Montrieux, Lionel. "Maintaining invariant traceability through bidirectional transformations", In: 34th International
Conference on Software Engineering, 02-09 June 2012, Zurich.
Yijun Yu, Thein Than Tun, and Bashar Nuseibeh (2011). "Specifying and detecting meaningful changes in programs". In: 26th IEEE/ACM International Conference On Automated Software Engineering, 6-11 Nov 2011, Lawrence, Kansas, USA, pp.
Yijun Yu, Arosha Bandara, Thein Thun Tun, and Bashar Nuseibeh. "Towards Learning to Detect Meaningful Changes in Software", In: Proceedings of International Workshop on Machine Learning Technologies in Software Engineering (MALETS'11), 2011.