用平滑敏感度提升私有选择精度，减少噪声干扰。
Differentially Private Selection using Smooth Sensitivity
- 基于平滑敏感度设计新机制，降低隐私保护噪声。
- 实测在分位数、决策树等任务中优于现有方法。
- 适合对隐私与精度要求高的数据查询场景。
差分隐私选择机制为从有限集合R中识别得分最高的元素r提供强隐私保障，基于数据依赖的效用函数。尽管选择查询在数据科学中至关重要，但现有方法多数依赖全局敏感度实现差分隐私（DP），导致噪声过大，影响后续推断。为此，本文提出平滑噪声最大值（SNM）机制，利用平滑敏感度，在理论上给出更紧的期望误差上界，优于基于全局敏感度的方法。实验证明，SNM在分位数选择、贪心决策树和随机森林三个应用场景中均比当前最优方法更准确。
原文摘要 · Abstract (English)
Differentially private selection mechanisms offer strong privacy guarantees for queries aiming to identify the top-scoring element r from a finite set R, based on a dataset-dependent utility function. While selection queries are fundamental in data science, few mechanisms effectively ensure their privacy. Furthermore, most approaches rely on global sensitivity to achieve differential privacy (DP), which can introduce excessive noise and impair downstream inferences. To address this limitation, we propose the Smooth Noisy Max (SNM) mechanism, which leverages smooth sensitivity to yield provably tighter (upper bounds on) expected errors compared to global sensitivity-based methods. Empirical results demonstrate that SNM is more accurate than state-of-the-art differentially private selection methods in three applications: percentile selection, greedy decision trees, and random forests.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。