Algorithms for Privacy-Preserving Machine Learning in JAX
☆161Mar 23, 2026Updated this week
Alternatives and similar repositories for jax_privacy
Users that are interested in jax_privacy are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- ☆10Jun 1, 2022Updated 3 years ago
- ☆15Jun 5, 2023Updated 2 years ago
- Code for Auditing DPSGD☆39Feb 15, 2022Updated 4 years ago
- Fast, memory-efficient, scalable optimization of deep learning with differential privacy☆140Jan 22, 2026Updated 2 months ago
- Computationally friendly hyper-parameter search with DP-SGD☆25Jan 7, 2025Updated last year
- ☆37Mar 16, 2022Updated 4 years ago
- [NeurIPS 2023] Differentially Private Image Classification by Learning Priors from Random Processes☆12Jun 12, 2023Updated 2 years ago
- ☆12Oct 2, 2023Updated 2 years ago
- Repo for the paper "Bounding Training Data Reconstruction in Private (Deep) Learning".☆11Jun 16, 2023Updated 2 years ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆32Apr 25, 2022Updated 3 years ago
- ☆20Jun 1, 2022Updated 3 years ago
- Private Evolution: Generating DP Synthetic Data without Training [ICLR 2024, ICML 2024 Spotlight]☆110Nov 8, 2025Updated 4 months ago
- ☆80May 22, 2022Updated 3 years ago
- A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.☆76Feb 15, 2024Updated 2 years ago
- autodp: A flexible and easy-to-use package for differential privacy☆279Dec 5, 2023Updated 2 years ago
- Public implementation of the paper "On the Importance of Difficulty Calibration in Membership Inference Attacks".☆16Dec 1, 2021Updated 4 years ago
- Analytic calibration for differential privacy with Gaussian perturbations☆51Oct 7, 2018Updated 7 years ago
- Differentially Private Diffusion Models☆106Dec 26, 2023Updated 2 years ago
- DP-FTRL from "Practical and Private (Deep) Learning without Sampling or Shuffling" for centralized training.☆35May 30, 2025Updated 9 months ago
- ☆25Apr 15, 2024Updated last year
- Training PyTorch models with differential privacy☆1,912Mar 16, 2026Updated last week
- Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.☆704Apr 26, 2025Updated 10 months ago
- ☆23Dec 15, 2022Updated 3 years ago
- Library for training machine learning models with privacy for training data☆2,003Updated this week
- ☆27Dec 15, 2022Updated 3 years ago
- Research and experimental code related to Opacus, an open-source library for training PyTorch models with Differential Privacy☆18Oct 9, 2024Updated last year
- A machine-learning-based tool for discovering differential privacy violations in black-box algorithms.☆23May 26, 2022Updated 3 years ago
- The core library of differential privacy algorithms powering the OpenDP Project.☆411Updated this week
- Differentially-private transformers using HuggingFace and Opacus☆147Aug 28, 2024Updated last year
- ☆45Nov 10, 2019Updated 6 years ago
- Code for fast dpsgd implementations in JAX/TF☆59Oct 12, 2022Updated 3 years ago
- Code for computing tight guarantees for differential privacy☆23Mar 3, 2023Updated 3 years ago
- Lint for privacy☆28Sep 5, 2022Updated 3 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆135Dec 8, 2022Updated 3 years ago
- Code for the paper "Evading Black-box Classifiers Without Breaking Eggs" [SaTML 2024]☆21Apr 15, 2024Updated last year
- ☆78May 28, 2022Updated 3 years ago
- Supporting code for "Parallel Streaming Wasserstein Barycenters"☆10Nov 14, 2017Updated 8 years ago
- An awesome list of papers on privacy attacks against machine learning☆633Mar 18, 2024Updated 2 years ago
- ☆19Feb 18, 2024Updated 2 years ago