Precision medicine is an emerging approach for disease treatment and prevention that
delivers personalized care to individual patients by considering their genetic make-
ups, medical histories, environments, and lifestyles. Despite the rapid advancement of
precision medicine and its considerable promise, several underlying technological chal-
lenges 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 sce-
nario 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. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/14055 |
| Date | 12 July 2022 |
| Creators | Moghaddam, Parisa |
| Contributors | Traore, Issa |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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