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Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

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Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics. / Kahrobaei, Delaram; Wood, Alexander; Najarian, Kayvan.

In: ACM Comput. Surv., 2020.

Research output: Contribution to journalArticlepeer-review

Harvard

Kahrobaei, D, Wood, A & Najarian, K 2020, 'Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics', ACM Comput. Surv..

APA

Kahrobaei, D., Wood, A., & Najarian, K. (2020). Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics. ACM Comput. Surv..

Vancouver

Kahrobaei D, Wood A, Najarian K. Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics. ACM Comput. Surv. 2020.

Author

Kahrobaei, Delaram ; Wood, Alexander ; Najarian, Kayvan. / Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics. In: ACM Comput. Surv. 2020.

Bibtex - Download

@article{c25a7b173ee14ec4af9eec44361c636a,
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. ",
author = "Delaram Kahrobaei and Alexander Wood and Kayvan Najarian",
note = "{\textcopyright} 2019 Association for Computing Machinery. This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy. Further copying may not be permitted; contact the publisher for details.",
year = "2020",
language = "English",
journal = "ACM Comput. Surv.",
issn = "0360-0300",
publisher = "Association for Computing Machinery (ACM)",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

AU - Kahrobaei, Delaram

AU - Wood, Alexander

AU - Najarian, Kayvan

N1 - © 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.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

M3 - Article

JO - ACM Comput. Surv.

JF - ACM Comput. Surv.

SN - 0360-0300

ER -