个性化隐私预算效果有限，简单阈值法更优
Limits of Personalizing Differential Privacy Budgets
- 用阈值操作选择有效隐私预算，替代复杂个性化分配
- 在混合数据集和双级隐私需求下，仅提升常数倍性能
- 揭示任意隐私要求下的理论上限与最佳增益区间
差分隐私中的核心挑战是如何在满足隐私要求的同时最大化数据效用。一种自然且广泛研究的解决方案是使用个性化隐私预算，即不同主体可采用不同预算。本文表明，个性化预算存在重大局限；对于均值估计任务，决定性因素并非完全个性化，而是选择合适的有效隐私预算。我们提出一种简单的阈值算子即可实现最优效果。相比该基准，完全个性化机制的收益极为有限。具体而言，在混合私有与公开数据集及具有两级隐私要求的私有数据场景中，性能提升仅为常数倍。我们还建立了上界，并识别出任意隐私要求下的最大增益区域。
原文摘要 · Abstract (English)
A key technical difficulty in differential privacy is selecting a privacy budget that satisfies privacy requirements while maximizing utility. A natural and well-studied workaround is to use personalized privacy budgets, which may differ across agents. In this paper, we show that personalized budgets come with major limitations and that for mean estimation, the dominant factor is not full personalization, but rather choosing the right effective privacy budget. This can be achieved through a simple thresholding operator that we describe. Compared with this thresholding baseline, the gains obtained by fully personalized mechanisms are limited. In particular, we precisely quantify the constant-factor improvement in settings with mixed private and public datasets and in private datasets with two levels of privacy requirements. We also establish upper bounds and identify regimes of maximal gain for arbitrary privacy requirements.
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