# Classification under local differential privacy

arXiv: https://arxiv.org/abs/1912.04629 · HTML: https://mlsanity.com/paper/1912.04629/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 9 of 9)

- [Optimal Locally Private Nonparametric Classification with Public Data](https://mlsanity.com/paper/2311.11369/) (2023)
- [Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints](https://mlsanity.com/paper/2406.20088/) (2024)
- [Private Statistical Estimation of Many Quantiles](https://mlsanity.com/paper/2302.06943/) (2023)
- [Learning with User-Level Local Differential Privacy](https://mlsanity.com/paper/2405.17079/) (2024)
- [On Rate-Optimal Partitioning Classification from Observable and from Privatised Data](https://mlsanity.com/paper/2312.14889/) (2023)
- [Locally Private Nonparametric Contextual Multi-armed Bandits](https://mlsanity.com/paper/2503.08098/) (2025)
- [Classification Under Local Differential Privacy with Model Reversal and Model Averaging](https://mlsanity.com/paper/2602.05797/) (2026)
- [SLDP: Semi-Local Differential Privacy for Density-Adaptive Analytics](https://mlsanity.com/paper/2602.18910/) (2026)
- [On public and private binary classification with metric space valued predictors](https://mlsanity.com/paper/2605.31184/) (2026)

## Abstract (arXiv)

We consider the binary classification problem in a setup that preserves the privacy of the original sample. We provide a privacy mechanism that is locally differentially private and then construct a classifier based on the private sample that is universally consistent in Euclidean spaces. Under stronger assumptions, we establish the minimax rates of convergence of the excess risk and see that they are slower than in the case when the original sample is available.