ltzheng / data-privacy Goto Github PK
View Code? Open in Web Editor NEWPreserve data privacy with k-anonymity (samarati & mondrian), differential privacy, federated learning, paillier homomorphic encryption, etc.
License: MIT License
Preserve data privacy with k-anonymity (samarati & mondrian), differential privacy, federated learning, paillier homomorphic encryption, etc.
License: MIT License
Dear author, I saw your code on github and found it very interesting, I want to study it further, but there is no literature corresponding to the code for reference is very headache, please have literature related to the code?
Dear author, when I reproduced your code on my device recently, there was a small problem, can I ask you for advice? When I run the code in paillier mode, the GPU is NVIDIA-A100-80GB-PCIe(mig-3g.40gb):1, and the cpu is AMD EPYC 7713 64-Core Processor. However, the encryption time I run is about 630s, the decryption time is about 178s, and the overall training time is 4100s, which is 2.3 times longer than the experimental data you provided. When I run DP mode and plain mode, the experimental data is basically similar to what you provided. Why does this happen when I run Paillier? If you reply, be very grateful!
请问一下,联邦学习中加密的是梯度矩阵,我使用pailliar无法加密,应该如何解决?能否帮忙指导一下?如果可以提供指导十分感谢
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