Establishing Verifiable Trust in Collaborative Health Research

Abstract

Collaborative health research environments usually involve sharing private health data between a number of participants, including researchers at different institutions. Inclusion of AI systems as participants in this environment allows predictive analytics to be applied on the research data and the provision of better diagnoses. However, the growing number of researchers and AI systems working together raises the problem of protecting the privacy of data contributors and managing the trust among participants, which affects the overall collaboration effort. In this thesis, we propose an architecture that utilizes blockchain technology for enabling verifiable trust in collaborative health research environments so that participants who do not necessarily trust each other can effectively collaborate to achieve a research goal. Provenance management of research data and privacy auditing are key components of the architecture that allow participants’ actions and their compliance with privacy policies to be checked across the research pipeline. The architecture supports distributed trust between participants through a Linked Data-based blockchain model that allows tamper-proof audit logs to be created to preserve log integrity and participant non-repudiation. To maintain the integrity of the audit logs, we investigate the state-of-the-art methods of generating cryptographic hashes for RDF datasets. We demonstrate an efficient method of computing integrity proofs that construct a sorted Merkle tree for growing RDF datasets based on timestamps (as a key) that are extractable from the dataset. Evaluations of our methods through experimental realizations and analyses of their resiliency to common security threats are provided.