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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.19700 |
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| _version_ | 1866916864940048384 |
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| author | Lautrup, Anton Danholt Rajabinasab, Muhammad Hyrup, Tobias Zimek, Arthur Schneider-Kamp, Peter |
| author_facet | Lautrup, Anton Danholt Rajabinasab, Muhammad Hyrup, Tobias Zimek, Arthur Schneider-Kamp, Peter |
| contents | We propose a new framework for generating cross-sectional synthetic datasets via disjoint generative models. In this paradigm, a dataset is partitioned into disjoint subsets that are supplied to separate instances of generative models. The results are then combined post hoc by a joining operation that works in the absence of common variables/identifiers. The success of the framework is demonstrated through several case studies and examples on tabular data that helps illuminate some of the design choices that one may make. The principal benefit of disjoint generative models is significantly increased privacy at only a low utility cost. Additional findings include increased effectiveness and feasibility for certain model types and the possibility for mixed-model synthesis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19700 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Disjoint Generative Models Lautrup, Anton Danholt Rajabinasab, Muhammad Hyrup, Tobias Zimek, Arthur Schneider-Kamp, Peter Machine Learning We propose a new framework for generating cross-sectional synthetic datasets via disjoint generative models. In this paradigm, a dataset is partitioned into disjoint subsets that are supplied to separate instances of generative models. The results are then combined post hoc by a joining operation that works in the absence of common variables/identifiers. The success of the framework is demonstrated through several case studies and examples on tabular data that helps illuminate some of the design choices that one may make. The principal benefit of disjoint generative models is significantly increased privacy at only a low utility cost. Additional findings include increased effectiveness and feasibility for certain model types and the possibility for mixed-model synthesis. |
| title | Disjoint Generative Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.19700 |