Rethinking Benchmarks for Differentially Private Image Classification
Fuente:
arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866912851289964544 |
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| author | Mokhtari, Sabrina Kodeiri, Sara Mohapatra, Shubhankar Tramèr, Florian Kamath, Gautam |
| author_facet | Mokhtari, Sabrina Kodeiri, Sara Mohapatra, Shubhankar Tramèr, Florian Kamath, Gautam |
| contents | We revisit benchmarks for differentially private image classification. We suggest a comprehensive set of benchmarks, allowing researchers to evaluate techniques for differentially private machine learning in a variety of settings, including with and without additional data, in convex settings, and on a variety of qualitatively different datasets. We further test established techniques on these benchmarks in order to see which ideas remain effective in different settings. Finally, we create a publicly available leader board for the community to track progress in differentially private machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17189 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Rethinking Benchmarks for Differentially Private Image Classification Mokhtari, Sabrina Kodeiri, Sara Mohapatra, Shubhankar Tramèr, Florian Kamath, Gautam Machine Learning We revisit benchmarks for differentially private image classification. We suggest a comprehensive set of benchmarks, allowing researchers to evaluate techniques for differentially private machine learning in a variety of settings, including with and without additional data, in convex settings, and on a variety of qualitatively different datasets. We further test established techniques on these benchmarks in order to see which ideas remain effective in different settings. Finally, we create a publicly available leader board for the community to track progress in differentially private machine learning. |
| title | Rethinking Benchmarks for Differentially Private Image Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.17189 |