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arXiv · 2601.17189

Rethinking Benchmarks for Differentially Private Image Classification

Sabrina Mokhtari·Sara Kodeiri·Shubhankar Mohapatra·Florian Tramèr·Gautam Kamath
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Abstract

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.

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BibTeXRIS

Sabrina Mokhtari, Sara Kodeiri, Shubhankar Mohapatra, Florian Tramèr, Gautam Kamath. 2026-01-23. Rethinking Benchmarks for Differentially Private Image Classification. https://arxiv.org/abs/2601.17189

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