We lifted 15 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| haiphanNJIT/SecureSGD | canonical | 3 of 3 |
| haiphanNJIT/StoBatch | pwc_unofficial | 2 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| avg_pool | Ran | haiphanNJIT/StoBatch/StoBatchCIFAR10/StoBatch_cifar10.py code served (permissive licence) · get_code("67a83a4977e5900b") |
| comp_sigma | Ran | haiphanNJIT/SecureSGD/MNIST/SecureSGD.py pointer only (licence: NONE) · get_code("5a277c84f6cc91ea") |
| compute_sigma | Ran | haiphanNJIT/SecureSGD/MNIST/SecureSGD.py pointer only (licence: NONE) · get_code("c8a866778827a03d") |
| fixed_padding | Ran | haiphanNJIT/StoBatch/StoBatchTinyImageNet/resnet_utils.py code served (permissive licence) · get_code("7fa95a4ad6afe798") |
| parse_time | Ran | haiphanNJIT/SecureSGD/MNIST/SecureSGD.py pointer only (licence: NONE) · get_code("0d51b5ad039c5e1b") |
| batch_norm | Not yet run | haiphanNJIT/StoBatch/StoBatchTinyImageNet/resnet_utils.py code served (permissive licence) · get_code("29e0dcee4d607b62") |
| bias_variable | Not yet run | haiphanNJIT/StoBatch/MNIST/StoBatch.py code served (permissive licence) · get_code("aafb9bf91f6ef802") |
| conv2d | Not yet run | haiphanNJIT/StoBatch/CIFAR10/StoBatch.py code served (permissive licence) · get_code("2c2112993af8d032") |
| conv2d_fixed_padding | Not yet run | haiphanNJIT/StoBatch/StoBatchTinyImageNet/resnet_utils.py code served (permissive licence) · get_code("60ed1730c5f4efa0") |
| generateIdLMNoise | Not yet run | haiphanNJIT/StoBatch/StoBatchTinyImageNet/StoBatch_resnet_pretrain.py code served (permissive licence) · get_code("991a7403637402a8") |
| generateNoise | Not yet run | haiphanNJIT/StoBatch/StoBatchTinyImageNet/StoBatch_resnet_pretrain.py code served (permissive licence) · get_code("9c54b765a044a6b6") |
| lossDPSGD | Not yet run | haiphanNJIT/StoBatch/StoBatchTinyImageNet/SSGD_loss.py code served (permissive licence) · get_code("6764ccff0ce428ed") |
| max_out | Not yet run | haiphanNJIT/StoBatch/CIFAR10/StoBatch.py code served (permissive licence) · get_code("b7a8162db3a2edfb") |
| parametric_relu | Not yet run | haiphanNJIT/StoBatch/CIFAR10/StoBatch.py code served (permissive licence) · get_code("9a523e5bd9c79de4") |
| weight_variable | Not yet run | haiphanNJIT/StoBatch/MNIST/StoBatch.py code served (permissive licence) · get_code("2a775c69ec858b1e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM) to preserve differential privacy in deep neural networks, with provable robustness against adversarial examples. We first relax the constraint of the privacy budget in the traditional Gaussian Mechanism from (0, 1] to (0, \infty), with a new bound of the noise scale to preserve differential privacy. The noise in our mechanism can be arbitrarily redistributed, offering a distinctive ability to address the trade-off between model utility and privacy loss. To derive provable robustness, our HGM is applied to inject Gaussian noise into the first hidden layer. Then, a tighter robustness bound is proposed. Theoretical analysis and thorough evaluations show that our mechanism notably improves the robustness of differentially private deep neural networks, compared with baseline approaches, under a variety of model attacks.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1906.01444")
get_code_for_paper("1906.01444")
have("1906.01444")
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