Secure tumor classification by shallow neural network using homomorphic encryption

Secure tumor classification by shallow neural network using homomorphic encryption

Background: Disclosure of patients’ genetic information in the process of applying machine learning techniques for tumor classification hinders the privacy of personal information. Homomorphic Encryption (HE), which supports operations between encrypted data, can be used as one of the tools to perfo...

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Bibliographic Details
Main Authors: Cheon, J.H (Author), Cho, W. (Author), Choe, H. (Author), Hong, S. (Author), Park, J.H (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2022
Subjects:
algorithm
Algorithms
computer security
Computer Security
genome-wide association study
Genome-Wide Association Study
Homomorphic encryption
human
Humans
Multi-label classification
Neural network
Neural Networks, Computer
privacy
Privacy
Softmax activation
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Internet

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