Codes for CPL attacks

This is the prototype code for ESORICS 2020 A Framework for Evaluating Gradient Leakage Attacks in Federated Learning. The talk can be found here: talk.

Examples

  ours DLG
MNIST mnist_ours mnist_dlg
CIFAR10 cifar10_ours cifar10_dlg
LFW lfw_ours lfw_dlg

Here is a brief description of each file.

-LFW_Deep_Leakage_from_Gradients.ipynb: lfw implementation for DLG attack in (NIPS2019) “Deep leakage from gradients.”

-LFW_enhanced_random_ASR.ipynb: CPL attack with geometric initialization

-LFW_batch.ipynb: CPL attack in batch

-LFW128_enhanced_random_ASR.ipynb: CPL attack with resolution 128*128. Same applies to LFW64_enhanced_random_ASR.ipynb

-LFW_defense.ipynb: CPL attack under high-pass filter and additive noise

If you use our code, please cite:

Wei, Wenqi, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu. "A Framework for Evaluating Client Privacy Leakages in Federated Learning." In European Symposium on Research in Computer Security, pp. 545-566. Springer, Cham, 2020.