eth-sri / dp-sniper
A machine-learning-based tool for discovering differential privacy violations in black-box algorithms.
☆25Updated 2 years ago
Alternatives and similar repositories for dp-sniper:
Users that are interested in dp-sniper are comparing it to the libraries listed below
- CaPC is a method that enables collaborating parties to improve their own local heterogeneous machine learning models in a setting where b…☆26Updated 3 years ago
- Analytic calibration for differential privacy with Gaussian perturbations☆46Updated 6 years ago
- ☆64Updated 5 years ago
- Code for Canonne-Kamath-Steinke paper https://arxiv.org/abs/2004.00010☆60Updated 4 years ago
- Code for Auditing DPSGD☆37Updated 3 years ago
- A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.☆73Updated last year
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆131Updated 2 years ago
- Statistical Counterexample Detector for Differential Privacy☆28Updated 11 months ago
- Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget☆49Updated 7 years ago
- Code for computing tight guarantees for differential privacy☆23Updated 2 years ago
- Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"☆30Updated 2 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆33Updated 3 years ago
- Implementation of calibration bounds for differential privacy in the shuffle model☆23Updated 4 years ago
- ☆49Updated 4 years ago
- ☆14Updated last year
- ☆80Updated 2 years ago
- Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enfo…☆41Updated 11 months ago
- GBDT learning + differential privacy. Standalone C++ implementation of "DPBoost" (Li et al.). There are further hardened & SGX versions o…☆8Updated 2 years ago
- This work combines differential privacy and multi-party computation protocol to achieve distributed machine learning.☆26Updated 4 years ago
- Hadamard Response: Communication efficient, sample optimal, linear time locally private learning of distributions☆14Updated 4 years ago
- An implementation of "Data Synthesis via Differentially Private Markov Random Fields"☆13Updated last year
- Privacy-preserving Federated Learning with Trusted Execution Environments☆65Updated 2 years ago
- ☆33Updated last year
- Privacy Risks of Securing Machine Learning Models against Adversarial Examples☆44Updated 5 years ago
- Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"☆49Updated 3 years ago
- Differential Privacy Testing System☆22Updated 4 years ago
- Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)☆53Updated 5 years ago
- Code for ML Doctor☆88Updated 7 months ago
- Implementations of differentially private release mechanisms for graph statistics☆23Updated 2 years ago
- This is a python script to generate nice bibtex file for latex.☆16Updated 5 years ago