Tight Lower Bounds for Differentially Private Continual Counting

2026-09-15

AI Summary

本文解决了二叉树机制在纯差分隐私下渐近最优性的问题，通过证明最坏情况预期误差的紧下界，确立了其在差分隐私连续计数中的最优性。

Abstract

The Binary Tree Mechanism is a standard algorithm for differentially private continual counting, but its asymptotic optimality under pure differential privacy has remained unresolved since its introduction. We resolve this question. For fixed $0 < \varepsilon \le 1$, we prove asymptotically tight lower bounds of $Ω(\log^2 n)$ for worst-case expected $\ell_\infty$ error and $Ω(\log^3 n)$ for mean and maximum per-coordinate expected squared error. These bounds hold for arbitrary mechanisms, even when the entire stream is available in advance. The same lower bounds hold under approximate differential privacy whenever $δ\le n^{-c}$, for any fixed $c>0$. Our lower bounds match the Binary Tree Mechanism instantiated with Laplace noise, establishing its asymptotic optimality under both pure differential privacy and approximate differential privacy in the standard regime of $δ\ll1/n$. Our proof uses a single hard distribution with a bounded exponential score on a tree. A simple modification of the score allows the same framework to establish tight lower bounds for all three error measures.

Problem

• Differentially Private
• Continual Counting
• Binary Tree Mechanism
• Lower Bounds

Innovation

• Binary Tree Mechanism
• asymptotic optimality
• differential privacy
• lower bounds