Opinion: Revisiting synthetic data classifications from a privacy perspective
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915431320649728 |
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| author | Vallevik, Vibeke Binz Marshall, Serena Elizabeth Babic, Aleksandar Nygaard, Jan Franz |
| author_facet | Vallevik, Vibeke Binz Marshall, Serena Elizabeth Babic, Aleksandar Nygaard, Jan Franz |
| contents | Synthetic data is emerging as a cost-effective solution necessary to meet the increasing data demands of AI development, created either from existing knowledge or derived from real data. The traditional classification of synthetic data types into hybrid, partial or fully synthetic datasets has limited value and does not reflect the ever-increasing methods to generate synthetic data. The generation method and their source jointly shape the characteristics of synthetic data, which in turn determines its practical applications. We make a case for an alternative approach to grouping synthetic data types that better reflect privacy perspectives in order to facilitate regulatory guidance in the generation and processing of synthetic data. This approach to classification provides flexibility to new advancements like deep generative methods and offers a more practical framework for future applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03506 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Opinion: Revisiting synthetic data classifications from a privacy perspective Vallevik, Vibeke Binz Marshall, Serena Elizabeth Babic, Aleksandar Nygaard, Jan Franz Machine Learning Artificial Intelligence Synthetic data is emerging as a cost-effective solution necessary to meet the increasing data demands of AI development, created either from existing knowledge or derived from real data. The traditional classification of synthetic data types into hybrid, partial or fully synthetic datasets has limited value and does not reflect the ever-increasing methods to generate synthetic data. The generation method and their source jointly shape the characteristics of synthetic data, which in turn determines its practical applications. We make a case for an alternative approach to grouping synthetic data types that better reflect privacy perspectives in order to facilitate regulatory guidance in the generation and processing of synthetic data. This approach to classification provides flexibility to new advancements like deep generative methods and offers a more practical framework for future applications. |
| title | Opinion: Revisiting synthetic data classifications from a privacy perspective |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.03506 |