bargavj / EvaluatingDPML
This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
☆129Updated last year
Related projects ⓘ
Alternatives and complementary repositories for EvaluatingDPML
- Differential Privacy Preservation in Deep Learning under Model Attacks☆132Updated 3 years ago
- simple Differential Privacy in PyTorch☆48Updated 4 years ago
- A library for running membership inference attacks against ML models☆139Updated last year
- Analytic calibration for differential privacy with Gaussian perturbations☆44Updated 6 years ago
- Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget☆47Updated 6 years ago
- Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models☆123Updated 7 months ago
- Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)☆187Updated 7 years ago
- Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470☆146Updated 2 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆47Updated 3 years ago
- autodp: A flexible and easy-to-use package for differential privacy☆267Updated 11 months ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆30Updated 2 years ago
- Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)☆53Updated 5 years ago
- Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enfo…☆41Updated 7 months ago
- Implementation of calibration bounds for differential privacy in the shuffle model☆23Updated 4 years ago
- The code for "Improved Deep Leakage from Gradients" (iDLG).☆144Updated 3 years ago
- This repo implements several algorithms for learning with differential privacy.☆102Updated last year
- Implementation of the Model Inversion Attack introduced with Model Inversion Attacks that Exploit Confidence Information and Basic Counte…☆81Updated last year
- ☆79Updated 2 years ago
- Differentially Private Optimization for PyTorch 👁🙅♀️☆184Updated 4 years ago
- Differential private machine learning☆179Updated 2 years ago
- Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"☆38Updated 5 years ago
- ☆43Updated 3 years ago
- Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.☆52Updated 5 years ago
- This repository contains the codes for first large-scale investigation of Differentially Private Convex Optimization algorithms.☆63Updated 6 years ago
- ☆45Updated 5 years ago
- Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (https://arxiv.org/abs/16…☆42Updated 2 years ago
- ☆13Updated last year
- Code for NIPS'2017 paper☆49Updated 4 years ago
- Curated notebooks on how to train neural networks using differential privacy and federated learning.☆66Updated 3 years ago
- CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)☆71Updated 3 years ago