Democratizing Tabular Data Access with an Open$\unicode{x2013}$Source Synthetic$\unicode{x2013}$Data SDK
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912706213183488 |
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| author | Krchova, Ivona Vieyra, Mariana Vargas Scriminaci, Mario Sidorenko, Andrey |
| author_facet | Krchova, Ivona Vieyra, Mariana Vargas Scriminaci, Mario Sidorenko, Andrey |
| contents | Machine learning development critically depends on access to high-quality data. However, increasing restrictions due to privacy, proprietary interests, and ethical concerns have created significant barriers to data accessibility. Synthetic data offers a viable solution by enabling safe, broad data usage without compromising sensitive information. This paper presents the MOSTLY AI Synthetic Data Software Development Kit (SDK), an open-source toolkit designed specifically for synthesizing high-quality tabular data. The SDK integrates robust features such as differential privacy guarantees, fairness-aware data generation, and automated quality assurance into a flexible and accessible Python interface. Leveraging the TabularARGN autoregressive framework, the SDK supports diverse data types and complex multi-table and sequential datasets, delivering competitive performance with notable improvements in speed and usability. Currently deployed both as a cloud service and locally installable software, the SDK has seen rapid adoption, highlighting its practicality in addressing real-world data bottlenecks and promoting widespread data democratization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00718 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Democratizing Tabular Data Access with an Open$\unicode{x2013}$Source Synthetic$\unicode{x2013}$Data SDK Krchova, Ivona Vieyra, Mariana Vargas Scriminaci, Mario Sidorenko, Andrey Machine Learning Machine learning development critically depends on access to high-quality data. However, increasing restrictions due to privacy, proprietary interests, and ethical concerns have created significant barriers to data accessibility. Synthetic data offers a viable solution by enabling safe, broad data usage without compromising sensitive information. This paper presents the MOSTLY AI Synthetic Data Software Development Kit (SDK), an open-source toolkit designed specifically for synthesizing high-quality tabular data. The SDK integrates robust features such as differential privacy guarantees, fairness-aware data generation, and automated quality assurance into a flexible and accessible Python interface. Leveraging the TabularARGN autoregressive framework, the SDK supports diverse data types and complex multi-table and sequential datasets, delivering competitive performance with notable improvements in speed and usability. Currently deployed both as a cloud service and locally installable software, the SDK has seen rapid adoption, highlighting its practicality in addressing real-world data bottlenecks and promoting widespread data democratization. |
| title | Democratizing Tabular Data Access with an Open$\unicode{x2013}$Source Synthetic$\unicode{x2013}$Data SDK |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.00718 |