HOPE: Software Defect Prediction Model Construction Method via Homomorphic Encryption
Software defect prediction can predict the defective modules in the project in advance, which is helpful to optimize the allocation of test resources. Recently, privacy protection for datasets and models has gradually attracted the attention of researchers. In this study, we are the first to apply h...
Main Authors: | Chi Yu, Zixuan Ding, Xiang Chen |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9425532/ |
Similar Items
-
Logistic regression over encrypted data from fully homomorphic encryption
by: Hao Chen, et al.
Published: (2018-10-01) -
Ensemble Method for Privacy-Preserving Logistic Regression Based on Homomorphic Encryption
by: Jung Hee Cheon, et al.
Published: (2018-01-01) -
Logistic regression model training based on the approximate homomorphic encryption
by: Andrey Kim, et al.
Published: (2018-10-01) -
Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption
by: Sergiu Carpov, et al.
Published: (2020-07-01) -
A privacy-preserving parallel and homomorphic encryption scheme
by: Min Zhaoe, et al.
Published: (2017-04-01)