facebookresearch / privacy_lint
Lint for privacy
☆26Updated 2 years ago
Related projects: ⓘ
- Research and experimental code related to Opacus, an open-source library for training PyTorch models with Differential Privacy☆17Updated 2 months ago
- A concise primer on Differential Privacy☆28Updated 4 years ago
- Algorithms for Privacy-Preserving Machine Learning in JAX☆87Updated 3 months ago
- Code for fast dpsgd implementations in JAX/TF☆58Updated last year
- PyTorch implementation of parity loss as constraints function to realize the fairness of machine learning.☆71Updated last year
- Library and experiments for attacking machine learning in discrete domains☆45Updated last year
- ☆23Updated 8 months ago
- Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19☆34Updated 3 years ago
- Code for Auditing DPSGD☆30Updated 2 years ago
- Github pages backend for https://differentialprivacy.org☆25Updated 3 months ago
- ☆35Updated 2 years ago
- Privacy Testing for Deep Learning☆183Updated last year
- ☆38Updated 2 years ago
- Cryptographically secure pseudorandom number generators for PyTorch☆106Updated 4 months ago
- Privacy-preserving XGBoost Inference☆47Updated last year
- ☆17Updated 4 years ago
- FairPrep is a design and evaluation framework for fairness-enhancing interventions that treats data as a first-class citizen.☆11Updated last year
- Discount jupyter.☆40Updated 2 years ago
- TextHide: Tackling Data Privacy in Language Understanding Tasks☆30Updated 3 years ago
- Membership Inference Competition☆30Updated last year
- Implementation of membership inference and model inversion attacks, extracting training data information from an ML model. Benchmarking …☆98Updated 4 years ago
- A software package for privacy-preserving generation of a synthetic twin to a given sensitive data set.☆46Updated 2 weeks ago
- Repo for the paper "Bounding Training Data Reconstruction in Private (Deep) Learning".☆10Updated last year
- ☆15Updated 4 years ago
- Inspect ML Pipelines in Python in the form of a DAG☆68Updated 6 months ago
- Differentially-private transformers using HuggingFace and Opacus☆108Updated 3 weeks ago
- This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"☆26Updated last year
- Code for paper: "Spinning Language Models: Risks of Propaganda-as-a-Service and Countermeasures"☆21Updated 2 years ago
- A community-run reference for state-of-the-art adversarial example defenses.☆49Updated last year
- 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