awslabs / sagemaker-privacy-for-nlp
A solution that helps apply a privacy preserving mechanism to NLP data, using Amazon SageMaker.
☆17Updated 2 years ago
Related projects: ⓘ
- A software package for privacy-preserving generation of a synthetic twin to a given sensitive data set.☆46Updated 2 weeks ago
- Privacy-preserving XGBoost Inference☆47Updated last year
- FedNLP: An Industry and Research Integrated Platform for Federated Learning in Natural Language Processing, Backed by FedML, Inc. The Pre…☆223Updated 2 years ago
- ☆23Updated 8 months ago
- ☆49Updated 3 years ago
- Federated gradient boosted decision tree learning☆68Updated last year
- dpart: General, flexible, and scalable framework for differentially private synthetic data generation, developed by hazy.☆23Updated 3 months ago
- A concise primer on Differential Privacy☆28Updated 4 years ago
- Federated Learning Utilities and Tools for Experimentation☆182Updated 8 months ago
- Python language bindings for smartnoise-core.☆75Updated last year
- ☆31Updated last year
- A privacy preserving NLP framework☆197Updated last year
- Differentially-private transformers using HuggingFace and Opacus☆108Updated 3 weeks ago
- Secure collaborative training and inference for XGBoost.☆105Updated last year
- Privacy-preserving generative deep neural networks support clinical data sharing☆104Updated 5 years ago
- This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.☆117Updated 3 years ago
- A Natural Language Interface to Explainable Boosting Machines☆59Updated 2 months ago
- A codebase that makes differentially private training of transformers easy.☆151Updated last year
- Jenga is an experimentation library that allows data science practititioners and researchers to study the effect of common data corruptio…☆35Updated last year
- Platform enabling Rapid Annotation for Clinical Entity Recognition☆49Updated 2 years ago
- Automatically exported from code.google.com/p/negex☆36Updated 2 years ago
- Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.☆37Updated 5 months ago
- Weak supervision methods for extracting real world evidence from EHRs☆32Updated 4 years ago
- Privacy-Preserving Bandits (MLSys'20)☆22Updated last year
- A collection of implementations of fair ML algorithms☆11Updated 6 years ago
- A toolbox for differentially private data generation☆127Updated last year
- SDNist: Benchmark data and evaluation tools for data synthesizers.☆31Updated 3 months ago
- ☆17Updated 3 years ago
- Preprint/draft article/blog on some explainable machine learning misconceptions. WIP!☆28Updated 5 years ago
- automatic data slicing☆34Updated 3 years ago