By the same authors

From the same journal

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Research output: Contribution to journalArticlepeer-review

Full text download(s)

  • main

    1.43 MB, PDF document

Author(s)

Department/unit(s)

Publication details

JournalACM Comput. Surv.
DateSubmitted - 2019
DateAccepted/In press - 20 Apr 2020
DateE-pub ahead of print - 22 Apr 2020
DatePublished (current) - 2020
Number of pages35
Early online date22/04/20
Original languageEnglish

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.

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.

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations