Permissioned Blockchain-based Framework for Ranking Synthetic Data Generators
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910444075089920 |
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| author | Veeraragavan, Narasimha Raghavan Tabatabaei, Mohammad Hossein Elvatun, Severin Vallevik, Vibeke Binz Larønningen, Siri Nygård, Jan F |
| author_facet | Veeraragavan, Narasimha Raghavan Tabatabaei, Mohammad Hossein Elvatun, Severin Vallevik, Vibeke Binz Larønningen, Siri Nygård, Jan F |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07196 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Permissioned Blockchain-based Framework for Ranking Synthetic Data Generators Veeraragavan, Narasimha Raghavan Tabatabaei, Mohammad Hossein Elvatun, Severin Vallevik, Vibeke Binz Larønningen, Siri Nygård, Jan F Databases Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning 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. |
| title | Permissioned Blockchain-based Framework for Ranking Synthetic Data Generators |
| topic | Databases Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2405.07196 |