AI

Privacy Requirements: Present & Future

Abstract

Software systems are increasingly more and more open, handle large amounts of personal or other sensitive data and are intricately linked with the daily lives of individuals and communities. This poses a range of privacy requirements. Such privacy requirements are typically treated as instances of requirements pertaining to compliance, traceability, access control, verification or usability. Though important, such approaches assume that the scope for the privacy requirements can be established a-priori and that such scope does not vary drastically once the system is deployed. User data and information, however, exists in an open, hyper-connected and potentially “unbounded” environment. Furthermore, “privacy requirements - present” and “privacy requirements - future” may differ significantly as the privacy implications are often emergent a-posteriori. Effective treatment of privacy requirements, therefore, requires techniques and approaches that fit with the inherent openness and fluidity of the environment through which user data and information flows are shared. This paper surveys state of the art and present some potential directions in the way privacy requirements should be treated. We reflect on the limitations of existing approaches with regards to unbounded privacy requirements and highlight a set of key challenges for requirements engineering research with regards to managing privacy in such unbounded settings.