GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
Authors: Dingfan Chen, Tribhuvanesh Orekondy, Mario Fritz
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we find our approach consistently outperforms state-of-the-art approaches across multiple metrics (e.g., sample quality) and datasets. Extensi ve evaluations on various datasets demonstrate that our method significantly improves the sample quality of privacy-preserving data over state-of-the-art approaches. To validate the applicability of our method to high-dimensional data, we conduct experiments on image datasets. |
| Researcher Affiliation | Academia | 1 CISPA Helmholtz Center for Information Security 2 Max Planck Institute for Informatics |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. It describes the methods in text and uses mathematical formulas. |
| Open Source Code | No | The paper mentions using source code for baseline methods but does not state that the code for its own methodology is being released or provide a link to it. |
| Open Datasets | Yes | In line with previous works, we use MNIST [23] and Fashion-MNIST [40] dataset. We conduct experiments on the Federated EMNIST dataset [9] and compare our GS-WGAN with Fed-Avg GAN [3]. |
| Dataset Splits | No | The paper mentions using 60k privately-generated data points for downstream tasks and evaluating during training iterations, but it does not specify explicit train/validation/test splits (e.g., percentages or counts) for the original datasets used in their experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'scikit-learn [34]' but does not provide a version number for it or any other software dependencies crucial for replication. |
| Experiment Setup | Yes | Better model architecture: While previous works are limited to shallow networks and thereby bottle-necking generated sample quality, our framework allows stable training with a complex model architecture (DCGAN [35] architecture for the discriminator, Res Net architecture (adapted from Big GAN [8]) for the generator) to help improve the sample quality; Discriminator warm-starting: To bootstrap the training process, we pre-train discriminators along with a non-private generator for a few steps, and we subsequently train the private generator using the warm-starting values of the discriminators. We evaluate the sample quality of our method considering multiple choices of subsampling rates (γ [1/250, 1/500, 1/1000, 1/1500]) over the training iterations. For a fair comparison, we evaluate all methods with a privacy budget of (ε, δ)=(10, 10 5) (consistently used in previous works) over 60K generated samples. |