Algorithms for Privacy-Preserving Machine Learning in JAX
☆179Jun 29, 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 4 years ago
- ☆15Jun 5, 2023Updated 3 years ago
- Code for Auditing DPSGD☆39Feb 15, 2022Updated 4 years ago
- Fast, memory-efficient, scalable optimization of deep learning with differential privacy☆146Jan 22, 2026Updated 5 months ago
- Computationally friendly hyper-parameter search with DP-SGD☆27Jan 7, 2025Updated last year
- Simple, predictable pricing with DigitalOcean hosting • AdAlways know what you'll pay with monthly caps and flat pricing. Enterprise-grade infrastructure trusted by 600k+ customers.
- ☆37Mar 16, 2022Updated 4 years ago
- ☆13Oct 2, 2023Updated 2 years ago
- [NeurIPS 2023] Differentially Private Image Classification by Learning Priors from Random Processes☆12Jun 12, 2023Updated 3 years ago
- Repo for the paper "Bounding Training Data Reconstruction in Private (Deep) Learning".☆12Jun 16, 2023Updated 3 years ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆32Apr 25, 2022Updated 4 years ago
- A codebase that makes differentially private training of transformers easy.☆190Dec 9, 2022Updated 3 years ago
- ☆20Jun 1, 2022Updated 4 years ago
- Private Evolution: Generating DP Synthetic Data without Model Training [ICML 2026, ICLR 2024, ICML 2024 Spotlight]☆113Updated this week
- ☆80May 22, 2022Updated 4 years ago
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.☆78Feb 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".☆17Dec 1, 2021Updated 4 years ago
- ☆28Nov 28, 2023Updated 2 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
- Private Adaptive Optimization with Side Information (ICML '22)☆16Jun 23, 2022Updated 4 years ago
- DP-FTRL from "Practical and Private (Deep) Learning without Sampling or Shuffling" for centralized training.☆37Jun 10, 2026Updated 3 weeks ago
- ☆27Apr 15, 2024Updated 2 years ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- Training PyTorch models with differential privacy☆1,936Jun 14, 2026Updated 2 weeks ago
- ☆23Dec 15, 2022Updated 3 years ago
- Library for training machine learning models with privacy for training data☆2,014May 11, 2026Updated last month
- Code repo for the paper "Privacy-aware Compression for Federated Data Analysis".☆18May 31, 2023Updated 3 years ago
- ☆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 4 years ago
- The core library of differential privacy algorithms powering the OpenDP Project.☆422Jun 26, 2026Updated last week
- Differentially-private transformers using HuggingFace and Opacus☆149Aug 28, 2024Updated last year
- GPU virtual machines on DigitalOcean Gradient AI • AdGet to production fast with high-performance AMD and NVIDIA GPUs you can spin up in seconds. The definition of operational simplicity.
- ☆46Nov 10, 2019Updated 6 years ago
- Code for fast dpsgd implementations in JAX/TF☆59Oct 12, 2022Updated 3 years ago
- Gradient-Leakage Resilient Federated Learning☆14Jul 25, 2022Updated 3 years ago
- Code for computing tight guarantees for differential privacy☆23Mar 3, 2023Updated 3 years ago
- Lint for privacy☆30Sep 5, 2022Updated 3 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆136Dec 8, 2022Updated 3 years ago
- This code reproduces the results of the paper, "Measuring Data Leakage in Machine-Learning Models with Fisher Information"☆48Aug 17, 2021Updated 4 years ago