A Fast Gaussian Mechanism under Continual Observation, with Applications

2026-06-10

AI Summary

This work addresses the problem of differentially private release of partial entries of high-dimensional vectors under continuous observation. The authors propose a novel data structure that integrates Brownian bridges with differentially private CountSketch, enabling efficient approximate queries on arbitrary subsets of a dynamically updated vector at any time point. The key innovation lies in a Gaussian noise mechanism that samples correlated noise with correct covariance in constant time, thereby overcoming the $O(\log T)$ per-query time complexity bottleneck inherent in traditional binary tree-based approaches. Empirical evaluations demonstrate that the method significantly outperforms existing solutions in tasks such as orthogonal range counting and join size estimation, achieving stronger accuracy, improved space efficiency, and tighter high-probability error bounds while preserving rigorous differential privacy guarantees.

Abstract

We consider the problem of privately releasing a $k$-dimensional vector under updates: Starting with a zero vector, at times $t_1, t_2,\dots$ the vector is updated by adding $x^{(1)}, x^{(2)},\dots$, respectively. For positive integers $T$, $k$ we model the updates as a data set $\{(t_i, x^{(i)})\}_i$, where $t_i \in [T]$ and $x^{(i)} \in B_k$ (the $k$-dimensional unit ball). Two such data sets are said to be neighboring if their symmetric difference has size at most $1$. The continual release consists of the sum $A^{(t)} = \sum_{i \; : \; t_i \leq t} x^{(i)}$ for each time step $t=1,\dots,T$. Classical continual release techniques allow us to release an approximation of $A^{(1)},\dots,A^{(T)}$ with additive noise of magnitude $\text{polylog}(T)$, computed in time $O(kT)$, even in the on-line, adaptive case where data is continually revealed for the current time step. Motivated by private sketching techniques, we consider the setting where only a \emph{subset} of entries in $A^{(t)}$ need to be released at time step $t$. Our new result is that it is possible to sample any desired entry in a given noise vector in \emph{constant time} while reproducing exactly the distribution of the binary tree mechanism with Gaussian noise. The improvement on the known time bound of $O(\log T)$ comes from a new data structure that allows us to sample a new noise value with the correct correlations in constant time using Brownian bridges. We present two data management applications, of independent interest, that use our technique in conjunction with differentially private CountSketches: 1) A dynamic data structure for orthogonal range counting queries with a better privacy/accuracy/space trade-off than previous data structures, and 2) Join size estimation, where in addition we show improved high-probability bounds.

Problem

• differential privacy
• continual observation
• Gaussian mechanism
• private release
• dynamic data structures

Innovation

• continual observation
• Gaussian mechanism
• Brownian bridge
• differentially private CountSketch
• constant-time sampling