Private Continual Counting of Unbounded Streams

• URL: http://arxiv.org/abs/2506.15018v1
• Date: Tue, 17 Jun 2025 23:09:53 GMT
• Title: Private Continual Counting of Unbounded Streams
• Authors: Ben Jacobsen, Kassem Fawaz,
• Abstract summary: We study the problem of differentially private continual counting in the unbounded setting where the input size $n$ is not known in advance.<n>Using the common doubling trick' avoids knowledge of $n$ but leads to suboptimal and non-smooth error.<n>We introduce novel matrix factorizations based on logarithmic perturbations of the function $frac1sqrt1-z$ studied in prior works.
• Score: 11.941250828872189
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of differentially private continual counting in the unbounded setting where the input size $n$ is not known in advance. Current state-of-the-art algorithms based on optimal instantiations of the matrix mechanism cannot be directly applied here because their privacy guarantees only hold when key parameters are tuned to $n$. Using the common `doubling trick' avoids knowledge of $n$ but leads to suboptimal and non-smooth error. We solve this problem by introducing novel matrix factorizations based on logarithmic perturbations of the function $\frac{1}{\sqrt{1-z}}$ studied in prior works, which may be of independent interest. The resulting algorithm has smooth error, and for any $\alpha > 0$ and $t\leq n$ it is able to privately estimate the sum of the first $t$ data points with $O(\log^{2+2\alpha}(t))$ variance. It requires $O(t)$ space and amortized $O(\log t)$ time per round, compared to $O(\log(n)\log(t))$ variance, $O(n)$ space and $O(n \log n)$ pre-processing time for the nearly-optimal bounded-input algorithm of Henzinger et al. (SODA 2023). Empirically, we find that our algorithm's performance is also comparable to theirs in absolute terms: our variance is less than $1.5\times$ theirs for $t$ as large as $2^{24}$.

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