论文标题
差异隐私的界定机制
A bounded-noise mechanism for differential privacy
论文作者
论文摘要
我们提出了一个渐近最佳的$（ε，δ）$差异化的私人机制，用于回答多个，自适应地问，$Δ$敏感的查询，解决了Steinke和Ullman [2020]的猜想。我们的算法具有重要的优势，即它在每个查询中添加独立的有界噪声，从而提供一个绝对误差。此外，我们将算法应用于自适应数据分析中，并获得了改进的保证，用于回答有关使用有限样本的一些基本分布的多个查询。数值计算表明，在许多标准设置中，有限的噪声机制优于高斯机制。
We present an asymptotically optimal $(ε,δ)$ differentially private mechanism for answering multiple, adaptively asked, $Δ$-sensitive queries, settling the conjecture of Steinke and Ullman [2020]. Our algorithm has a significant advantage that it adds independent bounded noise to each query, thus providing an absolute error bound. Additionally, we apply our algorithm in adaptive data analysis, obtaining an improved guarantee for answering multiple queries regarding some underlying distribution using a finite sample. Numerical computations show that the bounded-noise mechanism outperforms the Gaussian mechanism in many standard settings.
