yuxiangw / autodpLinks
autodp: A flexible and easy-to-use package for differential privacy
β274Updated last year
Alternatives and similar repositories for autodp
Users that are interested in autodp are comparing it to the libraries listed below
Sorting:
- Differentially Private Optimization for PyTorch ππ ββοΈβ186Updated 5 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.β135Updated 2 years ago
- Differential private machine learningβ194Updated 3 years ago
- A library for running membership inference attacks against ML modelsβ148Updated 2 years ago
- Differential Privacy Preservation in Deep Learning under Model Attacksβ135Updated 4 years ago
- Analytic calibration for differential privacy with Gaussian perturbationsβ48Updated 6 years ago
- Privacy Preserving Vertical Federated Learningβ218Updated 2 years ago
- A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.β73Updated last year
- β80Updated 3 years ago
- Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470β151Updated 2 years ago
- A Simulator for Privacy Preserving Federated Learningβ94Updated 4 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"β49Updated 4 years ago
- Algorithms to recover input data from their gradient signal through a neural networkβ293Updated 2 years ago
- Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budgetβ49Updated 7 years ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"β32Updated 3 years ago
- simple Differential Privacy in PyTorchβ48Updated 5 years ago
- Code for Canonne-Kamath-Steinke paper https://arxiv.org/abs/2004.00010β60Updated 5 years ago
- This repository contains the codes for first large-scale investigation of Differentially Private Convex Optimization algorithms.β63Updated 6 years ago
- Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)β299Updated 10 months ago
- Breaching privacy in federated learning scenarios for vision and textβ295Updated last year
- Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)β193Updated 7 years ago
- Python package for simple implementations of state-of-the-art LDP frequency estimation algorithms. Contains code for our VLDB 2021 Paper.β74Updated last year
- Algorithms for Privacy-Preserving Machine Learning in JAXβ94Updated 2 months ago
- Simulate a federated setting and run differentially private federated learning.β379Updated 3 months ago
- Fast, memory-efficient, scalable optimization of deep learning with differential privacyβ121Updated last month
- Implementation of dp-based federated learning framework using PyTorchβ300Updated 2 years ago
- This repo implements several algorithms for learning with differential privacy.β108Updated 2 years ago
- Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enfoβ¦β41Updated last year
- Implementation of calibration bounds for differential privacy in the shuffle modelβ22Updated 4 years ago
- Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Modelsβ126Updated last year