DPBayes / twinify
A software package for privacy-preserving generation of a synthetic twin to a given sensitive data set.
☆51Updated 6 months ago
Alternatives and similar repositories for twinify:
Users that are interested in twinify are comparing it to the libraries listed below
- ☆35Updated last year
- Privacy preserving synthetic data generation workflows☆20Updated 3 years ago
- A toolbox for differentially private data generation☆130Updated last year
- ☆39Updated 2 years ago
- A curated list of awesome resources for creating synthetic data☆41Updated 3 years ago
- An implementation of the tools described in the paper entitled "Graphical-model based estimation and inference for differential privacy"☆100Updated this week
- A collection of implementations of fair ML algorithms☆12Updated 7 years ago
- Editing machine learning models to reflect human knowledge and values☆124Updated last year
- Privacy-preserving XGBoost Inference☆48Updated last year
- ☆264Updated 11 months ago
- pyCANON is a Python library and CLI to assess the values of the parameters associated with the most common privacy-preserving techniques.☆33Updated last week
- A Unified Framework for Quantifying Privacy Risk in Synthetic Data according to the GDPR☆80Updated last week
- SDNist: Benchmark data and evaluation tools for data synthesizers.☆34Updated last week
- Algorithms for generating synthetic data☆16Updated 9 months ago
- Comparing fairness-aware machine learning techniques.☆160Updated 2 years ago
- Python language bindings for smartnoise-core.☆76Updated 2 years ago
- ☆23Updated last year
- Tools and service for differentially private processing of tabular and relational data☆262Updated 2 months ago
- A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation.☆223Updated last week
- Inspect ML Pipelines in Python in the form of a DAG☆70Updated last year
- Evaluate real and synthetic datasets against each other☆86Updated 2 months ago
- Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)☆82Updated 2 years ago
- Privacy-Preserving Machine Learning (PPML) Tutorial☆37Updated 9 months ago
- Metrics to evaluate quality and efficacy of synthetic datasets.☆226Updated this week
- UCLANesl - NIST Differential Privacy Challenge (Match 3)☆23Updated 5 years ago
- this repo might get accepted☆29Updated 4 years ago
- Code samples and documentation for SmartNoise differential privacy tools☆132Updated 2 years ago
- PipelineDP is a Python framework for applying differentially private aggregations to large datasets using batch processing systems such a…☆274Updated last month
- Model Agnostic Counterfactual Explanations☆87Updated 2 years ago
- Public home of pycorels, the python binding to CORELS☆77Updated 4 years ago