eth-sri / dp-sniper
A machine-learning-based tool for discovering differential privacy violations in black-box algorithms.
☆24Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for dp-sniper
- CaPC is a method that enables collaborating parties to improve their own local heterogeneous machine learning models in a setting where b…☆26Updated 2 years ago
- ☆42Updated 3 years ago
- ☆62Updated 5 years ago
- Analytic calibration for differential privacy with Gaussian perturbations☆44Updated 6 years ago
- Code for Canonne-Kamath-Steinke paper https://arxiv.org/abs/2004.00010☆59Updated 4 years ago
- Code for computing tight guarantees for differential privacy☆21Updated last year
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆129Updated last year
- Code for Auditing DPSGD☆35Updated 2 years ago
- A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.☆67Updated 8 months ago
- Hadamard Response: Communication efficient, sample optimal, linear time locally private learning of distributions☆14Updated 4 years ago
- ☆78Updated 2 years ago
- Statistical Counterexample Detector for Differential Privacy☆28Updated 7 months ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆30Updated 2 years ago
- Implementations of differentially private release mechanisms for graph statistics☆17Updated 2 years ago
- This repository contains the codes for first large-scale investigation of Differentially Private Convex Optimization algorithms.☆63Updated 6 years ago
- Differential Privacy Testing System☆20Updated 4 years ago
- Library for training globally-robust neural networks.☆28Updated last year
- Implementation of calibration bounds for differential privacy in the shuffle model☆23Updated 4 years ago
- ☆13Updated last year
- Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing☆49Updated 3 years ago
- GBDT learning + differential privacy. Standalone C++ implementation of "DPBoost" (Li et al.). There are further hardened & SGX versions o…☆8Updated 2 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆47Updated 3 years ago
- A library for running membership inference attacks against ML models☆137Updated last year
- Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget☆47Updated 6 years ago
- ☆33Updated last year
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆31Updated 3 years ago
- Code for the IEEE S&P 2018 paper 'Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning'☆52Updated 3 years ago
- autodp: A flexible and easy-to-use package for differential privacy☆265Updated 11 months ago
- An implementation of the tools described in the paper entitled "Graphical-model based estimation and inference for differential privacy"☆92Updated this week
- Byzantine-resilient distributed SGD with TensorFlow.☆37Updated 3 years ago