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SAFE-PASS: stewardship, advocacy, fairness and empowerment in privacy, accountability, security, and safety for vulnerable groups

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dc.contributor.authorRay, Indrajit, author
dc.contributor.authorThuraisingham, Bhavani, author
dc.contributor.authorVaidya, Jaideep, author
dc.contributor.authorMehrotra, Sharad, author
dc.contributor.authorAtluri, Vijayalakshmi, author
dc.contributor.authorRay, Indrakshi, author
dc.contributor.authorKantarcioglu, Murat, author
dc.contributor.authorRaskar, Ramesh, author
dc.contributor.authorSalimi, Babak, author
dc.contributor.authorSimske, Steve, author
dc.contributor.authorVenkatasubramanian, Nalini, author
dc.contributor.authorSingh, Vivek, author
dc.contributor.authorACM, publisher
dc.date.accessioned2024-11-11T19:34:33Z
dc.date.available2024-11-11T19:34:33Z
dc.date.issued2023-05-24
dc.description.abstractOur 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.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationIndrajit Ray, Bhavani Thuraisingham, Jaideep Vaidya, Sharad Mehrotra, Vijayalakshmi Atluri, Indrakshi Ray, Murat Kantarcioglu, Ramesh Raskar, Babak Salimi, Steve Simske, Nalini Venkatasubramanian, and Vivek Singh. 2023. SAFE-PASS: Stewardship, Advocacy, Fairness and Empowerment in Privacy, Accountability, Security, and Safety for Vulnerable Groups. In Proceedings of the 28th ACM Symposium on Access Control Models and Technologies (SACMAT '23), June 7–9, 2023, Trento, Italy. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3589608.3593830
dc.identifier.doihttps://doi.org/10.1145/3589608.3593830
dc.identifier.urihttps://hdl.handle.net/10217/239533
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofPublications
dc.relation.ispartofACM DL Digital Library
dc.rights©Indrajit Ray, et al. ACM2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in SACMAT '23, https://dx.doi.org/10.1145/3589608.3593830.
dc.subjectprivacy
dc.subjectsecurity
dc.subjectusability
dc.subjectaccountability
dc.subjectfairness
dc.subjectmachine learning
dc.subjectvulnerable populations
dc.titleSAFE-PASS: stewardship, advocacy, fairness and empowerment in privacy, accountability, security, and safety for vulnerable groups
dc.typeText

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