2024/05/12 by Narasimha Raghavan Veeraragavan, Veeraragavan, Narasimha Raghavan, Mohammad Hossein Tabatabaei +9 · 1 citation
Computer Science · #Blockchain Technology Applications and Security #Cryptography and Security (cs.CR) #Databases (cs.DB) #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2405.07196
openalex publication_date 2024/05/12 · openalex created_date 2024/05/15 · openalex updated_date 2026/07/28
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.