open-algorithms-opal

The chapter explores the critical role of trust in digital societies by presenting innovative architectures like OPAL and ENIGMA that enable privacy-preserving data insights without sharing raw data, addressing challenges of privacy, security, and data siloing. It situates these technologies within the broader context of the New Deal on Data and the UN Data Revolution, emphasizing the transformative potential of trustworthy, real-time data combined with algorithmic decision-making to enhance governance, accountability, and societal wellbeing through a new social contract around data sharing.

The chapter introduces social capital accounting as a novel framework to quantify social connections influencing human well-being by leveraging computational social science and privacy-preserving data architectures like OPAL. It demonstrates the practical application of this framework through experimental analysis of call log data, achieving high accuracy in predicting trust, and discusses the scalability and societal benefits of transforming social capital into a real-world asset while maintaining privacy and security.

The chapter details a privacy-centric, layered data architecture for health IT that emphasizes moving algorithms to data repositories rather than sharing raw data, as exemplified by the open algorithms (OPAL) paradigm. It explores federated authorization and consent management frameworks such as OAuth2.0 and UMA, alongside cryptographic privacy-preserving computation techniques including multiparty computation, secret sharing, and hardware secure enclaves, enabling collaborative biomedical research and clinical trials while maintaining compliance with privacy regulations. The chapter also demonstrates the practical viability of differential privacy in predictive modeling and highlights the potential of these technologies to transform precision medicine initiatives and drug discovery through secure, interoperable, and privacy-preserving health data systems.