Open Access
ARTICLE
A Privacy Preserving Deep Linear Regression Scheme Based on Homomorphic Encryption
Danping Dong1, *, Yue Wu1, Lizhi Xiong1, Zhihua Xia1
1 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
* Corresponding Author: Danping Dong. Email: .
Journal on Big Data 2019, 1(3), 145-150. https://doi.org/10.32604/jbd.2019.08706
Abstract
This paper proposes a strategy for machine learning in the ciphertext domain.
The data to be trained in the linear regression equation is encrypted by SHE homomorphic
encryption, and then trained in the ciphertext domain. At the same time, it is guaranteed
that the error of the training results between the ciphertext domain and the plaintext domain
is in a controllable range. After the training, the ciphertext can be decrypted and restored to
the original plaintext training data.
Keywords
Cite This Article
D. Dong, Y. Wu, L. Xiong and Z. Xia, "A privacy preserving deep linear regression scheme based on homomorphic encryption,"
Journal on Big Data, vol. 1, no.3, pp. 145–150, 2019. https://doi.org/10.32604/jbd.2019.08706