提出可证明稳定的私有参数估计方法，实现理论最优的隐私保护统计量。
On Privately Estimating a Single Parameter
- 基于局部估计量稳定性构建新隐私机制
- 在大样本下达到实例最优的统计效率
- 在真实人口普查数据中验证了实用性
我们研究了大型参数模型中单个参数的差分隐私估计。尽管存在通用的私有估计器，本文提出的估计器基于新的局部估计量稳定性概念，可生成自身稳定性的私有证明。利用这些私有证明，我们设计出在计算和统计上都高效的机制，释放的私有统计量在样本量趋于无穷时几乎无法被改进，达到了实例最优。我们还在模拟数据和美国社区调查、美国人口普查等真实数据上检验了算法的实用性，明确了其适用场景并指出了未来改进方向。
原文摘要 · Abstract (English)
We investigate differentially private estimators for individual parameters within larger parametric models. While generic private estimators exist, the estimators we provide repose on new local notions of estimand stability, and these notions allow procedures that provide private certificates of their own stability. By leveraging these private certificates, we provide computationally and statistical efficient mechanisms that release private statistics that are, at least asymptotically in the sample size, essentially unimprovable: they achieve instance optimal bounds. Additionally, we investigate the practicality of the algorithms both in simulated data and in real-world data from the American Community Survey and US Census, highlighting scenarios in which the new procedures are successful and identifying areas for future work.
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