重新设计差分隐私图像分类评估标准，推动更全面的算法对比。
Rethinking Benchmarks for Differentially Private Image Classification
- 构建涵盖多种数据与场景的综合性评测基准
- 验证现有方法在不同设置下的实际有效性
- 提供公开排行榜，助力社区追踪技术进展
我们重新审视差分隐私图像分类的评测基准。提出一套全面的评测体系，支持在有无额外数据、凸优化场景以及多种不同类型数据集下评估差分隐私机器学习方法。通过在这些基准上测试已有技术，考察其在不同设置中的有效性。最后，建立公开可访问的排行榜，供社区持续跟踪差分隐私机器学习领域的进展。
原文摘要 · Abstract (English)
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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