https://doi.org/10.7490/f1000research.1119545.1
Poster
NOT PEER REVIEWED
Download
metrics
VIEWS
75
 
downloads
10
CITE
How to cite this poster:
Eo SHA and Park SH. HEaaN: A scalable privacy-preserving machine learning using homomorphic encryption for bioinformatician [version 1; not peer reviewed]. F1000Research 2023, 12:905 (poster) (https://doi.org/10.7490/f1000research.1119545.1)
NOTE: it is important to ensure the information in square brackets after the title is included in this citation.

HEaaN: A scalable privacy-preserving machine learning using homomorphic encryption for bioinformatician

Soo-Heang Abel Eo1, Song Hyeop Park
Author Affiliations
  • Metrics
  • 75 Views
  • 10 Downloads
 
Part of the gateway
Browse by related subjects
Published 29 Jul 2023

HEaaN: A scalable privacy-preserving machine learning using homomorphic encryption for bioinformatician

[version 1; not peer reviewed]

Soo-Heang Abel Eo1, Song Hyeop Park
Author Affiliations
1 CryptoLab Inc., South Korea
Presented at
Bioconductor Annual Conference (BioC), 2023
Abstract
Competing Interests

No competing interests were disclosed

Keywords
Homomorphic Encryption, Privacy-Preserving, Machine Learning, Logistic Regression, Python, R/Bioconductor
Comments
0 Comments
 
Sign In
If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password.

The email address should be the one you originally registered with F1000.

Email address not valid, please try again

You registered with F1000 via Google, so we cannot reset your password.

To sign in, please click here.

If you still need help with your Google account password, please click here.

You registered with F1000 via Facebook, so we cannot reset your password.

To sign in, please click here.

If you still need help with your Facebook account password, please click here.

Code not correct, please try again
Email us for further assistance.
Server error, please try again.