sunchang0124 / dp_cgans
A library to generate synthetic tabular or RDF data using Conditional Generative Adversary Networks (GANs) combined with Differential Privacy techniques.
☆37Updated 4 months ago
Alternatives and similar repositories for dp_cgans:
Users that are interested in dp_cgans are comparing it to the libraries listed below
- ☆35Updated last year
- Standardised Metrics and Methods for Synthetic Tabular Data Evaluation☆29Updated 6 months ago
- Generating Tabular Synthetic Data using State of the Art GAN architecture☆79Updated 4 years ago
- Synthetic data generation by a Variational AutoEncoder with Differential Privacy assessed using Synthetic Data Vault metrics☆45Updated last year
- A toolbox for differentially private data generation☆129Updated last year
- Supplemental Material for the ESANN 2019 Submission "Preserving privacy using synthetic data models and applications in health informatic…☆19Updated 4 years ago
- code for ehrMGAN☆19Updated last year
- COR-GAN: Correlation-Capturing Convolutional Neural Networks for Generating Synthetic Healthcare Records☆57Updated 4 years ago
- Official git for "CTAB-GAN: Effective Table Data Synthesizing"☆82Updated last year
- ☆29Updated 2 years ago
- Evaluate real and synthetic datasets against each other☆86Updated last month
- A Unified Framework for Quantifying Privacy Risk in Synthetic Data according to the GDPR☆76Updated 7 months ago
- Algorithms for generating synthetic data☆16Updated 8 months ago
- ☆20Updated 6 months ago
- ☆39Updated 2 years ago
- Official GitHub for CTAB-GAN+☆73Updated 9 months ago
- Differentially-private Wasserstein GAN implementation in PyTorch☆27Updated 5 years ago
- Repository for the results of my master thesis, about the generation and evaluation of synthetic data using GANs☆44Updated last year
- ☆22Updated 2 years ago
- An implementation of the tools described in the paper entitled "Graphical-model based estimation and inference for differential privacy"☆98Updated this week
- Source code of paper "Differentially Private Generative Adversarial Network"☆69Updated 6 years ago
- PrivGAN: Protecting GANs from membership inference attacks at low cost☆33Updated 8 months ago
- Differentially Private Synthetic Data Generation [DP-SDG] - Experimental Setups & Knowledge Base - WORK IN PROGRESS☆11Updated 2 years ago
- Differentially Private (tabular) Generative Models Papers with Code☆45Updated 7 months ago
- Python package to create adversarial agents for membership inference attacks againts machine learning models☆46Updated 6 years ago
- Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)☆82Updated 2 years ago
- DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks☆20Updated last year
- ☆26Updated last year
- A software package for privacy-preserving generation of a synthetic twin to a given sensitive data set.☆51Updated 5 months ago
- A principled library for tuning, training and evaluating tabular data synthesis on fidelity, privacy and utility.☆19Updated 2 months ago