Privately Estimating Monotone Statistics in Polynomial Time
We study efficient differentially private algorithms for estimating monotone statistics, i.e., statistics that are monotone under the addition of new observations. The starting point for our investigation is subsample-and-aggregate: a classical paradigm that partitions the dataset into blocks, estimates the statistic on each block, and then privately aggregates the estimates. While practical and generically applicable, this approach is quite data-hungry. We improve upon this framework for the class of monotone statistics -- compared to subsample-and-aggregate, our algorithms save a factor of $t$ in sample complexity and pay a factor of $e^t$ in running time, where $t>0$ is a tunable param...
| Authors | Gavin Brown, Ephraim Linder, Mahbod Majid et al. |
|---|---|
| Primary category | cs.CR |
| Published | 27 May 2026 |
| Last updated at source | 28 May 2026 |
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