awslabs / fast-differential-privacy
Fast, memory-efficient, scalable optimization of deep learning with differential privacy
☆120Updated 3 months ago
Alternatives and similar repositories for fast-differential-privacy:
Users that are interested in fast-differential-privacy are comparing it to the libraries listed below
- This repo implements several algorithms for learning with differential privacy.☆107Updated 2 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"☆31Updated 2 years ago
- Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.☆58Updated 6 months ago
- Analytic calibration for differential privacy with Gaussian perturbations☆47Updated 6 years ago
- Differentially-private transformers using HuggingFace and Opacus☆134Updated 7 months ago
- DP-FTRL from "Practical and Private (Deep) Learning without Sampling or Shuffling" for centralized training.☆29Updated 3 weeks ago
- autodp: A flexible and easy-to-use package for differential privacy☆274Updated last year
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆131Updated 2 years ago
- Differential private machine learning☆191Updated 3 years ago
- Algorithms for Privacy-Preserving Machine Learning in JAX☆93Updated 9 months ago
- Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"☆55Updated last year
- Code for ML Doctor☆87Updated 8 months ago
- Code repo for the paper "Privacy-aware Compression for Federated Data Analysis".☆18Updated last year
- ☆15Updated last year
- Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enfo…☆41Updated last year
- Code for Auditing DPSGD☆37Updated 3 years ago
- Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.☆59Updated 5 years ago
- Private Evolution: Generating DP Synthetic Data without Training [ICLR 2024, ICML 2024 Spotlight]☆94Updated last month
- ☆72Updated 2 years ago
- Privacy attacks on Split Learning☆40Updated 3 years ago
- A library for running membership inference attacks against ML models☆143Updated 2 years ago
- ☆80Updated 2 years ago
- ☆54Updated 2 years ago
- Differentially Private (tabular) Generative Models Papers with Code☆48Updated 9 months ago
- Multiple Frequency Estimation Under Local Differential Privacy in Python☆47Updated last year
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆49Updated 3 years ago
- Implementation of calibration bounds for differential privacy in the shuffle model☆23Updated 4 years ago
- simple Differential Privacy in PyTorch☆48Updated 4 years ago