1. Permissioned Blockchain-based Framework for Ranking Synthetic Data Generators
- Author
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Veeraragavan, Narasimha Raghavan, Tabatabaei, Mohammad Hossein, Elvatun, Severin, Vallevik, Vibeke Binz, Larønningen, Siri, and Nygård, Jan F
- Subjects
Computer Science - Databases ,Computer Science - Cryptography and Security ,Computer Science - Distributed, Parallel, and Cluster Computing ,Computer Science - Machine Learning - Abstract
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.
- Published
- 2024