Opening the stacks
Opening the stacks
A privacy-preserving data sharing framework where relying parties execute vetted algorithms on data within a trust network, minimizing direct attribute disclosure.
2 chapters across 2 books
The chapter explores the evolution and technical foundations of core identity protocols and systems essential for future transaction systems, including anonymous identity schemes and the development of IAM frameworks like Web-SSO and OAuth 2.0. It further examines identity federation models that enable scalable and trusted sharing of user attributes across organizations, highlighting the role of attribute providers and introducing the OPAL paradigm, which advances privacy-preserving data sharing through vetted algorithm execution, strong auditability, and user consent mechanisms.
The chapter advocates for the creation of a trusted ecosystem of data and AI centered on human-centric governance through data cooperatives, which empower individuals by preserving legal ownership of their personal data and enabling controlled, purpose-specific use. It details privacy-preserving technologies such as the OPAL paradigm and advanced machine learning techniques that protect data privacy while enabling AI analysis, alongside consent management frameworks like GDPR and UMA to ensure informed, multi-party consent. The chapter concludes by emphasizing the role of data cooperatives in issuing digitally signed, limited-purpose assertions that enhance individual control and privacy, positioning collective institutions as essential to redressing the imbalance of data exploitation in the digital economy.