Privately Counting Partially Ordered Data
Matthew Joseph, Mónica Ribero, Alexander Yu
2025Year
2Top-tier citations
Abstract
We consider differentially private counting when each data point consists of d bits satisfying a partial order. Our main technical contribution is a problem-specific K-norm mechanism that runs in time O(d 2 ). Experiments show that, depending on the partial order in question, our solution dominates existing pure differentially private mechanisms, and can reduce their error by an order of magnitude or more.
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Install the CLIlune papers fulltext 2bb0ab5c-4396-40d1-80d4-25aa550b2b7eCited by top-tier papers2
- Continual Release Moment Estimation with Differential PrivacyNikita P. Kalinin, Jalaj Upadhyay, Christoph H. LampertNeurIPS 2025 · 5 citations
- Approximate Differential Privacy of the ℓ2 MechanismMatthew Joseph, Alex Kulesza, Alexander YuICML 2025
Builds on3
- Private Query Release via the Johnson-Lindenstrauss TransformAleksandar NikolovSODA 2023 · 1 citation
- Strong self-concordance and samplingAditi Laddha, Yin Tat Lee, Santosh S. VempalaSTOC 2020
- The power of factorization mechanisms in local and central differential privacyAlexander Edmonds, Aleksandar Nikolov, Jonathan R. UllmanSTOC 2020
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