niklausliu / PPGANs-Privacy-preserving-GANs
PPGANs: Privacy-preserving Generative Adversarial Networks.
☆17Updated 2 years ago
Alternatives and similar repositories for PPGANs-Privacy-preserving-GANs
Users that are interested in PPGANs-Privacy-preserving-GANs are comparing it to the libraries listed below
Sorting:
- Official implementation of "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators" (NeurIPS 2020)☆69Updated 2 years ago
- Source code of paper "Differentially Private Generative Adversarial Network"☆69Updated 6 years ago
- Implementation of a differentially private generative adversarial network.☆11Updated 6 years ago
- Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data☆33Updated 6 years ago
- PrivGAN: Protecting GANs from membership inference attacks at low cost☆33Updated 10 months ago
- Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (https://arxiv.org/abs/16…☆43Updated 3 years ago
- [NeurIPS 2021] "G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators" by Yunhui Long*…☆30Updated 3 years ago
- Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"☆37Updated 6 years ago
- Official implementation of "GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models" (CCS 2020)☆47Updated 3 years ago
- simple Differential Privacy in PyTorch☆48Updated 4 years ago
- Python package to create adversarial agents for membership inference attacks againts machine learning models☆47Updated 6 years ago
- Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enfo…☆41Updated last year
- A library for running membership inference attacks against ML models☆147Updated 2 years ago
- Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)☆193Updated 7 years ago
- Public implementation of ICML'19 paper "White-box vs Black-box: Bayes Optimal Strategies for Membership Inference"☆16Updated 4 years ago
- Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470☆151Updated 2 years ago
- Privacy Risks of Securing Machine Learning Models against Adversarial Examples☆44Updated 5 years ago
- Membership Inference of Generative Models☆15Updated 5 years ago
- ☆24Updated 3 years ago
- code for TPDS paper "Towards Fair and Privacy-Preserving Federated Deep Models"☆31Updated 2 years ago
- Curated notebooks on how to train neural networks using differential privacy and federated learning.☆68Updated 4 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆49Updated 4 years ago
- ☆14Updated last year
- ☆37Updated 2 years ago
- Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)☆49Updated 2 years ago
- Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19☆34Updated 3 years ago
- Differentially-private Wasserstein GAN implementation in PyTorch☆28Updated 5 years ago
- ☆80Updated 2 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆134Updated 2 years ago
- Differentially Private Conditional Generative Adversarial Network☆31Updated 3 years ago