Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Delaram Kahrobaei; Alexander Wood; Kayvan Najarian

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Delaram Kahrobaei, Alexander Wood, Kayvan Najarian

Computer Science

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Number of pages	35
Journal	ACM Comput. Surv.
Early online date	22 Apr 2020
Publication status	Published - 2020

Bibliographical note

© 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.

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© 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.
Accepted author manuscript, 1.43 MB

Cite this

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title = "Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics",

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. ",

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