SAP-archive / security-research-differentially-private-generative-models
SAP Security research sample code and tutorials for generating differentially private synthetic datasets using generative deep learning models
☆23Updated 8 months ago
Related projects ⓘ
Alternatives and complementary repositories for security-research-differentially-private-generative-models
- Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data☆33Updated 5 years ago
- Differentially private release of semantic rich data☆35Updated 3 years ago
- UCLANesl - NIST Differential Privacy Challenge (Match 3)☆23Updated 5 years ago
- Source code of paper "Differentially Private Generative Adversarial Network"☆67Updated 5 years ago
- Differentially Private Conditional Generative Adversarial Network☆30Updated 3 years ago
- Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19☆34Updated 3 years ago
- ☆15Updated 4 years ago
- Differentially-private Wasserstein GAN implementation in PyTorch☆27Updated 5 years ago
- A toolbox for differentially private data generation☆129Updated last year
- Python package to create adversarial agents for membership inference attacks againts machine learning models☆47Updated 5 years ago
- ☆23Updated 9 months ago
- This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"☆26Updated 2 years ago
- ☆10Updated last year
- Related material on Federated Learning☆27Updated 4 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆129Updated last year
- A TensorFlow (Python 3) implementation of a differentially-private-GAN.☆20Updated 4 years ago
- A simple wrapper for the PATE analysis for Differential Privacy by Papernot, Song et al.☆9Updated 4 years ago
- Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (https://arxiv.org/abs/16…☆41Updated 2 years ago
- Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"☆38Updated 5 years ago
- A concise primer on Differential Privacy☆28Updated 4 years ago
- simple Differential Privacy in PyTorch☆48Updated 4 years ago
- ☆8Updated 7 years ago
- This repository contains the codes for first large-scale investigation of Differentially Private Convex Optimization algorithms.☆63Updated 6 years ago
- [NeurIPS 2020] Simple and practical private mean and covariance estimation.☆33Updated 4 years ago
- A library for running membership inference attacks against ML models☆137Updated last year
- ☆31Updated 6 years ago
- A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.☆67Updated 8 months ago
- Analytic calibration for differential privacy with Gaussian perturbations☆44Updated 6 years ago
- Code for NIPS'2017 paper☆49Updated 4 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆47Updated 3 years ago