google-deepmind / jax_privacy
Algorithms for Privacy-Preserving Machine Learning in JAX
☆92Updated 9 months ago
Alternatives and similar repositories for jax_privacy:
Users that are interested in jax_privacy are comparing it to the libraries listed below
- ☆80Updated 2 years ago
- Code for Auditing DPSGD☆37Updated 3 years ago
- A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.☆73Updated last year
- A codebase that makes differentially private training of transformers easy.☆171Updated 2 years ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆30Updated 2 years ago
- Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19☆34Updated 3 years ago
- Code for fast dpsgd implementations in JAX/TF☆59Updated 2 years ago
- ☆13Updated last year
- ☆18Updated 2 years ago
- ☆11Updated last year
- ☆27Updated 2 years ago
- ☆31Updated 6 months ago
- ☆10Updated 2 years ago
- Analytic calibration for differential privacy with Gaussian perturbations☆46Updated 6 years ago
- Fast, memory-efficient, scalable optimization of deep learning with differential privacy☆115Updated 2 months ago
- autodp: A flexible and easy-to-use package for differential privacy☆273Updated last year
- Federated posterior averaging implemented in JAX☆51Updated last year
- ☆36Updated 3 years ago
- DP-FTRL from "Practical and Private (Deep) Learning without Sampling or Shuffling" for centralized training.☆28Updated 7 months ago
- Research and experimental code related to Opacus, an open-source library for training PyTorch models with Differential Privacy☆17Updated 5 months ago
- ☆22Updated 2 years ago
- A library for running membership inference attacks against ML models☆142Updated 2 years ago
- CaPC is a method that enables collaborating parties to improve their own local heterogeneous machine learning models in a setting where b…☆26Updated 3 years ago
- ☆24Updated 2 years ago
- Differentially Private Optimization for PyTorch 👁🙅♀️☆184Updated 4 years ago
- Certified Removal from Machine Learning Models☆65Updated 3 years ago
- Code repo for the paper "Privacy-aware Compression for Federated Data Analysis".☆18Updated last year
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆131Updated 2 years ago
- Tilted Empirical Risk Minimization (ICLR '21)☆59Updated last year
- Private Adaptive Optimization with Side Information (ICML '22)☆16Updated 2 years ago