# Differentially Private Algorithms for Empirical Machine Learning

arXiv: https://arxiv.org/abs/1411.5428 · HTML: https://mlsanity.com/paper/1411.5428/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2014.

## Built on by (most cited first, 8 of 8)

- [DPAUC: Differentially Private AUC Computation in Federated Learning](https://mlsanity.com/paper/2208.12294/) (2022)
- [Federated Calibration and Evaluation of Binary Classifiers](https://mlsanity.com/paper/2210.12526/) (2022)
- [Better Private Linear Regression Through Better Private Feature Selection](https://mlsanity.com/paper/2306.00920/) (2023)
- [The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing](https://mlsanity.com/paper/2506.16666/) (2025)
- [Differentially Private AUC Computation in Vertical Federated Learning](https://mlsanity.com/paper/2205.12412/) (2022)
- [On the Computational Complexity of Private High-dimensional Model Selection](https://mlsanity.com/paper/2310.07852/) (2023)
- [Unleash the Power of Ellipsis: Accuracy-enhanced Sparse Vector Technique with Exponential Noise](https://mlsanity.com/paper/2407.20068/) (2024)
- [A Survey on Differential Privacy for SpatioTemporal Data in Transportation Research](https://mlsanity.com/paper/2407.15868/) (2024)

## Abstract (arXiv)

An important use of private data is to build machine learning classifiers. While there is a burgeoning literature on differentially private classification algorithms, we find that they are not practical in real applications due to two reasons. First, existing differentially private classifiers provide poor accuracy on real world datasets. Second, there is no known differentially private algorithm for empirically evaluating the private classifier on a private test dataset. In this paper, we develop differentially private algorithms that mirror real world empirical machine learning workflows. We consider the private classifier training algorithm as a blackbox. We present private algorithms for selecting features that are input to the classifier. Though adding a preprocessing step takes away some of the privacy budget from the actual classification process (thus potentially making it noisier and less accurate), we show that our novel preprocessing techniques significantly increase classifier accuracy on three real-world datasets. We also present the first private algorithms for empirically constructing receiver operating characteristic (ROC) curves on a private test set.