facebookresearch / Opacus-lab
Research and experimental code related to Opacus, an open-source library for training PyTorch models with Differential Privacy
☆17Updated 2 months ago
Related projects: ⓘ
- ☆77Updated 2 years ago
- A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.☆64Updated 7 months ago
- Algorithms for Privacy-Preserving Machine Learning in JAX☆87Updated 3 months ago
- Code for Auditing DPSGD☆30Updated 2 years ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆29Updated 2 years ago
- Fast, memory-efficient, scalable optimization of deep learning with differential privacy☆86Updated last week
- Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19☆34Updated 3 years ago
- ☆21Updated last year
- ☆16Updated 2 years ago
- ☆26Updated last year
- Code for fast dpsgd implementations in JAX/TF☆58Updated last year
- DP-FTRL from "Practical and Private (Deep) Learning without Sampling or Shuffling" for centralized training.☆24Updated last month
- Code for Canonne-Kamath-Steinke paper https://arxiv.org/abs/2004.00010☆59Updated 4 years ago
- ☆41Updated 3 years ago
- ☆23Updated 8 months ago
- Code repo for the paper "Privacy-aware Compression for Federated Data Analysis".☆15Updated last year
- Differentially Private Optimization for PyTorch 👁🙅♀️☆183Updated 4 years ago
- Analytic calibration for differential privacy with Gaussian perturbations☆44Updated 5 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆129Updated last year
- Github pages backend for https://differentialprivacy.org☆25Updated 3 months ago
- simple Differential Privacy in PyTorch☆48Updated 4 years ago
- This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"☆26Updated last year
- InstaHide: Instance-hiding Schemes for Private Distributed Learning☆50Updated 3 years ago
- This repository contains the codes for first large-scale investigation of Differentially Private Convex Optimization algorithms.☆63Updated 5 years ago
- [NeurIPS 2020] Simple and practical private mean and covariance estimation.☆33Updated 3 years ago
- autodp: A flexible and easy-to-use package for differential privacy☆260Updated 9 months ago
- ☆31Updated 2 weeks ago
- ☆38Updated 2 years ago
- Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"☆38Updated 5 years ago
- ☆24Updated 2 years ago