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Cited by 179Cryptography and Data SecurityPrivacy-Preserving Technologies in DataCloud Data Security Solutions

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

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Wood, Alexander, Najarian, Kayvan, Kahrobaei, Delaram·2021·ACM Computing Surveys
DOI 10.1145/3394658
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Abstract

Machine learning and statistical techniques are powerful tools for analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent free sharing of this data. Encryption techniques such as fully homomorphic encryption (FHE) enable evaluation over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and Naive Bayes have been implemented for privacy-preserving applications using medical data. These applications include classifying encrypted data and training models on encrypted data. FHE has also been shown to enable secure genomic algorithms, such as paternity and ancestry testing and privacy-preserving applications 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, and details on current open-source implementations are provided. The state of fully homomorphic encryption for privacy-preserving techniques in machine learning and bioinformatics is reviewed, along with descriptions of how these methods can be implemented in the encrypted domain.

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Authors
Wood, A., Najarian, K., & Kahrobaei, D.
Affiliations
  • University of Michigan (US)
  • University of York (GB)
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2020-08-25
Volume
53
Issue
4
Pages
1-35
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3394658
OpenAlex
W3042006926
Cited by
179
References
118
Citations by year
2026: 21 · 2025: 39 · 2024: 37 · 2023: 38 · 2022: 26 · 2021: 14 · 2020: 4
FWCI
8.05
Type
journal article
Language
en
Open access
Licence
Locations
  • ACM Computing Surveys (publishedVersion, cc-by-nc)
  • White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
Funders
Naval Research (N000141512164)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
Keywords
fully homomorphic encryption, privacy-preserving machine learning, medical data privacy, genomic data privacy, paternity testing, genetic ancestry testing, encrypted data classification

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