yuxiangw / autodp
autodp: A flexible and easy-to-use package for differential privacy
☆267Updated 11 months ago
Related projects ⓘ
Alternatives and complementary repositories for autodp
- Differentially Private Optimization for PyTorch 👁🙅♀️☆184Updated 4 years ago
- Differential Privacy Preservation in Deep Learning under Model Attacks☆132Updated 3 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆129Updated last year
- A library for running membership inference attacks against ML models☆139Updated last year
- Differential private machine learning☆179Updated 2 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆47Updated 3 years ago
- Privacy Preserving Vertical Federated Learning☆215Updated last year
- Analytic calibration for differential privacy with Gaussian perturbations☆44Updated 6 years ago
- Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget☆47Updated 6 years ago
- This repository contains the codes for first large-scale investigation of Differentially Private Convex Optimization algorithms.☆63Updated 6 years ago
- Breaching privacy in federated learning scenarios for vision and text☆270Updated 7 months ago
- Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models☆124Updated 7 months ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆30Updated 2 years ago
- Fast, memory-efficient, scalable optimization of deep learning with differential privacy☆100Updated this week
- Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470☆146Updated 2 years ago
- simple Differential Privacy in PyTorch☆48Updated 4 years ago
- Algorithms to recover input data from their gradient signal through a neural network☆274Updated last year
- A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.☆70Updated 9 months ago
- Code for Canonne-Kamath-Steinke paper https://arxiv.org/abs/2004.00010☆59Updated 4 years ago
- ☆79Updated 2 years ago
- An implementation of the tools described in the paper entitled "Graphical-model based estimation and inference for differential privacy"☆93Updated this week
- Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.☆607Updated this week
- Source code of paper "Differentially Private Generative Adversarial Network"☆67Updated 5 years ago
- Simulate a federated setting and run differentially private federated learning.☆363Updated 3 months ago
- Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)☆189Updated 7 years ago
- A Simulator for Privacy Preserving Federated Learning☆92Updated 3 years ago
- Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)☆273Updated 3 months ago
- This repo implements several algorithms for learning with differential privacy.☆102Updated last year
- A codebase that makes differentially private training of transformers easy.☆160Updated last year
- Python package for simple implementations of state-of-the-art LDP frequency estimation algorithms. Contains code for our VLDB 2021 Paper.☆71Updated last year