An Exposition of GPT Astra's Proof of Lower Bound on DP Continual Counting
cs.CR, cs.DS, cs.LG
Submitted: 2026-09-22
Updated: 2026-09-22
Comments: This is full proof of GPT generated proof for DP continual counting written in preprint https://arxiv.org/abs/2609.17650v2
License: http://creativecommons.org/licenses/by/4.0/
The gist: The goal of this note is to give a detailed proof, to the best of our understanding, of the recent presentation by Harrison and Leeman (arXiv:2609.17650v01 and arXiv:2609.17650v02) of the proof by
Terminology
Abstract
The goal of this note is to give a detailed proof, to the best of our understanding, of the recent presentation by Harrison and Leeman (arXiv:2609.17650v01 and arXiv:2609.17650v02) of the proof by Astra on the lower bound for differentially private continual counting. We believe a more natural and easy proof is possible and hope that this note will help in that effort. Prior to the initial preprint by Harrison and Leeman (arXiv:2609.17650v01), Bairaktari and Larsen (arXiv:2607.00876) gave an elegant proof to show a lower bound of Ω(3/2(n)) for both pure and approximate-DP continual counting, and in personal communication had informed us that they have a proof of optimal Ω(squared(n)) for pure-differential private continual counting as well. They have subsequently published their Ω(squared(n)) bound, which is now a joint work of Bairaktari, Dahl, and Larsen (arXiv:2607.00876v3). Their new result is an elegant extension of their technique for approximate-differential privacy. Although the two proofs are technically different, the Astra argument uses related tree geometry introduced in Bairaktari and Larsen.
Sources
- Tight Lower Bounds for Differentially Private Continual Counting
- The Binary Tree Mechanism is Optimal for Differentially Private Continual Counting
- Count on Your Elders: Laplace vs Gaussian Noise
- Correlated Noise Mechanisms for Differentially Private Learning
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