Kahrobaei, Delaram orcid.org/0000-0001-5467-7832, Wood, Alexander and Najarian, Kayvan (2020) Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics. ACM Comput. Surv.. ISSN 0360-0300
Abstract
Machine learning techniques are an excellent tool for the medical community to analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent the free sharing of this data. Encryption methods such as fully homomorphic encryption (FHE) provide a method evaluate over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for private prediction using medical data. FHE has also been shown to enable secure genomic algorithms, such as paternity testing, and secure application of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced. Details on current open-source implementations are provided, as is the state of FHE for privacy-preserving techniques in machine learning and bioinformatics and future growth opportunities for FHE.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Association for Computing Machinery. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 23 Apr 2020 11:10 |
Last Modified: | 16 Oct 2024 16:03 |
Status: | Published |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151333 |