DingfanChen / GS-WGAN
Official implementation of "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators" (NeurIPS 2020)
☆68Updated 2 years ago
Alternatives and similar repositories for GS-WGAN:
Users that are interested in GS-WGAN are comparing it to the libraries listed below
- Differentially Private Conditional Generative Adversarial Network☆30Updated 3 years ago
- Source code of paper "Differentially Private Generative Adversarial Network"☆69Updated 6 years ago
- [NeurIPS 2021] "G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators" by Yunhui Long*…☆30Updated 3 years ago
- simple Differential Privacy in PyTorch☆48Updated 4 years ago
- Official implementation of "GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models" (CCS 2020)☆47Updated 2 years ago
- ☆17Updated last year
- [CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long…☆37Updated 3 years ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆30Updated 2 years ago
- ☆80Updated 2 years ago
- Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"☆38Updated 6 years ago
- Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.☆58Updated 5 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆49Updated 3 years ago
- Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget☆49Updated 6 years ago
- Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data☆33Updated 6 years ago
- Python package to create adversarial agents for membership inference attacks againts machine learning models☆46Updated 6 years ago
- The code for "Improved Deep Leakage from Gradients" (iDLG).☆147Updated 3 years ago
- Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)☆53Updated 5 years ago
- CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)☆71Updated 3 years ago
- Code for the paper: Label-Only Membership Inference Attacks☆64Updated 3 years ago
- Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)☆48Updated 2 years ago
- R-GAP: Recursive Gradient Attack on Privacy [Accepted at ICLR 2021]☆34Updated 2 years ago
- ☆45Updated 5 years ago
- ☆24Updated 2 years ago
- Membership Inference Attacks and Defenses in Neural Network Pruning☆28Updated 2 years ago
- ☆13Updated last year
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆31Updated 3 years ago
- Example of the attack described in the paper "Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization"☆21Updated 5 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆130Updated 2 years ago
- Adversarial attacks and defenses against federated learning.☆15Updated last year
- ☆14Updated last year