# Differentially Private Learning of Geometric Concepts

arXiv: https://arxiv.org/abs/1902.05017 · HTML: https://mlsanity.com/paper/1902.05017/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [General Geospatial Inference with a Population Dynamics Foundation Model](https://mlsanity.com/paper/2411.07207/) (2024)
- [On the Sample Complexity of Privately Learning Axis-Aligned Rectangles](https://mlsanity.com/paper/2107.11526/) (2021)
- [Privately Learning Decision Lists and a Differentially Private Winnow](https://mlsanity.com/paper/2602.07370/) (2026)

## Abstract (arXiv)

We present differentially private efficient algorithms for learning union of polygons in the plane (which are not necessarily convex). Our algorithms achieve (α,β)-PAC learning and (ε,δ)-differential privacy using a sample of size tildeOleft(frac1αεklog dright), where the domain is [d]×[d] and k is the number of edges in the union of polygons.