Privacy Requirements: Present & Future

Abstract

Software systems are increasingly 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. This paper surveys state of the art and presents 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.

Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

“…Recent Table 1 AI/ML algorithm application for descriptive, predictive, and prescriptive risk analytics in edge computing literature confirms diverse cyber risks from IoT systems (Maple 2017), including risks in IoT ecosystems (Tanczer et al 2018) and IoT environments (Breza et al 2018), such as risk from smart homes (Eirini Anthi et al 2019;Ghirardello et al 2018), the Industrial IoT (Boyes et al 2018), and challenges in security metrics (Agyepong et al 2019). Cybersecurity solutions for specific IoT risks are also emerging at a fast rate, such as new models on opportunities and motivations for reducing cyber risk (Safa et al 2018), adaptive intrusion detection (E. Anthi et al 2018), security economic by design (Craggs and Rashid 2017), highlighting the privacy requirements (Anthonysamy et al 2017) and strategies for achieving privacy (Van Kleek et al 2018). Therefore, our methodology is based on mathematical principles and quantitative data.…”

Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains

“…This could be supported by standardisation of design (Nurse et al 2017) but risk assessing is still a key problem (Petar Radanliev et al 2020). The reason for this is that digital cyber supply chain networks need to be: secure, vigilant, resilient and fully integrated (Craggs and Rashid 2017) and encompass the security and privacy (Anthonysamy et al 2017).…”

“…The SME's digital supply chains need to: e) Encompass the security and privacy (Anthonysamy et al 2017), along with electronic and physical security of real-time data (Agyepong et al 2019).…”

Artificial intelligence in cyber physical systems

“…This requires a dynamic understanding of the network risk. In addition, new risk elements that require cognitive analytics also need to be quantified, such as intellectual property of digital information (Anthonysamy et al 2017) and the impact of media coverage (Tanczer et al 2018).…”