Related papers: A Fast Gaussian Mechanism under Continual Observation, with Applications

A Fast Gaussian Mechanism under Continual Observation, with Applications

URL: http://arxiv.org/abs/2606.11760v1
Date: Wed, 10 Jun 2026 07:36:58 GMT
Title: A Fast Gaussian Mechanism under Continual Observation, with Applications
Abstract summary: We consider the problem of privately releasing a $k$-dimensional vector under updates.<n>We release an approximation of $A(1),dots,A(T)$ with additive noise of magnitude $textpolylog(T)$.<n>We present two data management applications, of independent interest, that use our technique in conjunction with differentially private CountSketches.
Score: 10.345196226375455
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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