stefanrmmr / differentially_private_synthetic_dataLinks
Differentially Private Synthetic Data Generation [DP-SDG] - Experimental Setups & Knowledge Base - WORK IN PROGRESS
☆12Updated 3 years ago
Alternatives and similar repositories for differentially_private_synthetic_data
Users that are interested in differentially_private_synthetic_data are comparing it to the libraries listed below
Sorting:
- A toolbox for differentially private data generation☆131Updated 2 years ago
- Generating Tabular Synthetic Data using State of the Art GAN architecture☆80Updated 5 years ago
- Official GitHub for CTAB-GAN+☆80Updated last year
- Course Material for the Tutorial on Privacy Enhancing Technologies and PPML☆13Updated 4 years ago
- ☆40Updated 2 years ago
- Differentially Private (tabular) Generative Models Papers with Code☆53Updated last year
- Official git for "CTAB-GAN: Effective Table Data Synthesizing"☆91Updated last year
- Tools and service for differentially private processing of tabular and relational data☆284Updated 2 months ago
- ☆36Updated 2 years ago
- ☆24Updated last year
- A library providing general-purpose tools for estimating discrete distributions from noisy observations of their marginals.☆106Updated last month
- A curated list of awesome resources for creating synthetic data☆44Updated 3 years ago
- Benchmarking synthetic data generation methods.☆282Updated this week
- pyCANON is a Python library and CLI to assess the values of the parameters associated with the most common privacy-preserving techniques.☆45Updated last week
- Differentially-private transformers using HuggingFace and Opacus☆143Updated last year
- Differentially-private Wasserstein GAN implementation in PyTorch☆28Updated 6 years ago
- A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation.☆237Updated 3 months ago
- Privacy Preserving Machine Learning (Manning Early Access Program)☆33Updated 2 years ago
- UCLANesl - NIST Differential Privacy Challenge (Match 3)☆25Updated 6 years ago
- A Unified Framework for Quantifying Privacy Risk in Synthetic Data according to the GDPR☆92Updated last month
- nbsynthetic is simple and robust tabular synthetic data generation library for small and medium size datasets☆69Updated 2 years ago
- Privacy-Preserving Machine Learning (PPML) Tutorial☆41Updated last year
- Evaluate real and synthetic datasets against each other☆92Updated 3 months ago
- Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data☆34Updated 6 years ago
- Metrics to evaluate quality and efficacy of synthetic datasets.☆251Updated this week
- 📖 A curated list of resources dedicated to synthetic data☆137Updated 3 years ago
- ☆14Updated 4 years ago
- Implementation of local differential privacy mechanisms in Python language.☆29Updated 3 years ago
- The HyFed framework provides an easy-to-use API to develop federated, privacy-preserving machine learning algorithms.☆18Updated last month
- A software package for privacy-preserving generation of a synthetic twin to a given sensitive data set.☆56Updated last year