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Privately Counting Partially Ordered Data
October 09, 2024 Β· Declared Dead Β· π International Conference on Learning Representations
"No code URL or promise found in abstract"
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Authors
Matthew Joseph, MΓ³nica Ribero, Alexander Yu
arXiv ID
2410.06881
Category
cs.CR: Cryptography & Security
Citations
2
Venue
International Conference on Learning Representations
Last Checked
5 months ago
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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