SAFE-PASS: Stewardship, Advocacy, Fairness and Empowerment in Privacy, Accountability, Security, and Safety for Vulnerable Groups

Our vision is to achieve societally responsible secure and trustworthy cyberspace that puts algorithmic and technological checks and balances on the indiscriminate sharing and analysis of data. We achieve this vision in a holistic manner by framing research directions with four major considerations: (i) Expanding knowledge and understanding of security and privacy perceptions and expectations in vulnerable groups, which significantly contribute to their unwillingness to share data, and use that knowledge to drive research in (a) mitigating missing/imbalanced data problems, (b) understanding and modeling security and privacy risks of data sharing, and (c) modeling utility of data sharing. (ii) Developing a risk-adaptive, policy model capable of capturing and articulating security and privacy expectations of users that are relevant in a particular context and develops associated technology to ensure provenance and accountability. (iii) Developing robust AI/ML algorithms that are transparent and explainable with respect to fairness and bias to reduce/eliminate discrimination, misuse, privacy violations, or other cyber-crimes. (iv) Developing models and techniques for a nuanced, contextually adaptive, and graded privacy paradigm that allows trade-offs between privacy and utility. Towards this, in this paper we present the SAFE-PASS framework to provide Stewardship, Advocacy, Fairness and Empowerment in Privacy, Accountability, Security, and Safety for Vulnerable Groups.