google / embedding-tests
☆16Updated 5 years ago
Alternatives and similar repositories for embedding-tests
Users that are interested in embedding-tests are comparing it to the libraries listed below
Sorting:
- Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19☆34Updated 4 years ago
- TextHide: Tackling Data Privacy in Language Understanding Tasks☆31Updated 4 years ago
- Code for Auditing DPSGD☆37Updated 3 years ago
- Research and experimental code related to Opacus, an open-source library for training PyTorch models with Differential Privacy☆17Updated 7 months ago
- ☆10Updated last year
- ☆27Updated 4 years ago
- A simple wrapper for the PATE analysis for Differential Privacy by Papernot, Song et al.☆9Updated 5 years ago
- ☆18Updated 2 years ago
- This project's goal is to evaluate the privacy leakage of differentially private machine learning models.☆134Updated 2 years ago
- A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.☆73Updated last year
- Statistical Counterexample Detector for Differential Privacy☆28Updated last year
- This repository contains the codes for first large-scale investigation of Differentially Private Convex Optimization algorithms.☆64Updated 6 years ago
- Lint for privacy☆26Updated 2 years ago
- This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"☆27Updated 2 years ago
- Code repo for the paper "Privacy-aware Compression for Federated Data Analysis".☆19Updated last year
- A Simulator for Privacy Preserving Federated Learning☆94Updated 4 years ago
- Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models☆126Updated last year
- ☆31Updated 8 months ago
- Simple Hierarchical Count Sketch in Python☆20Updated 3 years ago
- ☆27Updated 2 years ago
- Algorithms for Privacy-Preserving Machine Learning in JAX☆94Updated last month
- ☆80Updated 2 years ago
- ☆9Updated 4 years ago
- A codebase that makes differentially private training of transformers easy.☆171Updated 2 years ago
- Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"☆37Updated 6 years ago
- Code for the paper "Weight Poisoning Attacks on Pre-trained Models" (ACL 2020)☆141Updated 3 years ago
- DP-FTRL from "Practical and Private (Deep) Learning without Sampling or Shuffling" for centralized training.☆29Updated last month
- ☆45Updated 5 years ago
- Code for Canonne-Kamath-Steinke paper https://arxiv.org/abs/2004.00010☆61Updated 4 years ago
- ☆18Updated 2 years ago