A Framework for Secure Logging and Analytics in Precision Healthcare Cloud-based Services

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dc.contributor.author Moghaddam, Parisa
dc.date.accessioned 2022-07-12T23:07:22Z
dc.date.available 2022-07-12T23:07:22Z
dc.date.copyright 2022 en_US
dc.date.issued 2022-07-12
dc.identifier.uri http://hdl.handle.net/1828/14055
dc.description.abstract 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. en_US
dc.language English eng
dc.language.iso en en_US
dc.rights Available to the World Wide Web en_US
dc.subject Precision healthcare en_US
dc.subject Cloud services en_US
dc.subject Homomorphic encryption en_US
dc.subject Machine learning en_US
dc.title A Framework for Secure Logging and Analytics in Precision Healthcare Cloud-based Services en_US
dc.type Thesis en_US
dc.contributor.supervisor Traore, Issa
dc.degree.department Department of Electrical and Computer Engineering en_US
dc.degree.level Master of Applied Science M.A.Sc. en_US
dc.identifier.bibliographicCitation P. Moghaddam, S. Iqbal and I. Traore, "A Framework for Secure Logging in Precision Healthcare Cloud-based Services," 2021 IEEE International Conference on Digital Health (ICDH), 2021, pp. 212-214, doi: 10.1109/ICDH52753.2021.00038. en_US
dc.identifier.bibliographicCitation Briguglio, W., Moghaddam, P., Yousef, W. A., Traoré, I., & Mamun, M. (2021). Machine learning in precision medicine to preserve privacy via encryption. Pattern Recognition Letters, 151, 148-154. en_US
dc.description.scholarlevel Graduate en_US

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