# Permissioned Blockchain-based Framework for Ranking Synthetic Data Generators

arXiv: https://arxiv.org/abs/2405.07196 · HTML: https://mlsanity.com/paper/2405.07196/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 67 references that are in the map.
- First posted on arXiv 12 May 2024.

## Builds on (most cited first, 13 of 13)

- [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021)
- [DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks](https://mlsanity.com/paper/2110.12884/) (2021)
- [Synthcity: facilitating innovative use cases of synthetic data in different data modalities](https://mlsanity.com/paper/2301.07573/) (2023)
- [Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains](https://mlsanity.com/paper/1801.10228/) (2018)
- [A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models](https://mlsanity.com/paper/2208.01230/) (2022)
- [TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data](https://mlsanity.com/paper/2207.05295/) (2022)
- [SurvivalGAN: Generating Time-to-Event Data for Survival Analysis](https://mlsanity.com/paper/2302.12749/) (2023)
- [Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcare](https://mlsanity.com/paper/2401.13716/) (2024)
- [Measuring the quality of Synthetic data for use in competitions](https://mlsanity.com/paper/1806.11345/) (2018)
- [Really Useful Synthetic Data -- A Framework to Evaluate the Quality of Differentially Private Synthetic Data](https://mlsanity.com/paper/2004.07740/) (2020)
- [Integration of Blockchain and Cloud of Things: Architecture, Applications and Challenges](https://mlsanity.com/paper/1908.09058/) (2019)
- [GDPR Compliant Blockchains-A Systematic Literature Review](https://mlsanity.com/paper/2104.00648/) (2021)
- [Understanding blockchain: definitions, architecture, design, and system comparison](https://mlsanity.com/paper/2207.02264/) (2022)

## Abstract (arXiv)

Synthetic data generation is increasingly recognized as a crucial solution to address data related challenges such as scarcity, bias, and privacy concerns. As synthetic data proliferates, the need for a robust evaluation framework to select a synthetic data generator becomes more pressing given the variety of options available. In this research study, we investigate two primary questions: 1) How can we select the most suitable synthetic data generator from a set of options for a specific purpose? 2) How can we make the selection process more transparent, accountable, and auditable? To address these questions, we introduce a novel approach in which the proposed ranking algorithm is implemented as a smart contract within a permissioned blockchain framework called Sawtooth. Through comprehensive experiments and comparisons with state-of-the-art baseline ranking solutions, our framework demonstrates its effectiveness in providing nuanced rankings that consider both desirable and undesirable properties. Furthermore, our framework serves as a valuable tool for selecting the optimal synthetic data generators for specific needs while ensuring compliance with data protection principles.