No abstract

Precision medicine is an emerging approach for disease treatment and prevention that delivers personalized care to individual patients by considering their genetic makeups, medical histories, environments, and lifestyles. Despite the rapid advancement of precision medicine and its considerable promise, several underlying technological challenges remain unsolved. One such challenge of great importance is the security and privacy of precision health-related data, such as genomic data and electronic health records, which stifle collaboration and hamper the full potential of machine-learning (ML) algorithms. To preserve data privacy while providing ML solutions, this thesis explores the feasibility of machine learning with encryption for precision healthcare datasets. Moreover, to ensure audit logs' integrity, we introduce a blockchain-based secure logging architecture for precision healthcare transactions. We consider a scenario that lets us send sensitive healthcare data into the cloud while preserving privacy by using homomorphic encryption and develop a secure logging framework for this precision healthcare service using Hyperledger Fabric. We test the architecture by generating a considerable volume of logs and show that our system is tamper-resistant and can ensure integrity.