# Classification with Partially Private Features

arXiv: https://arxiv.org/abs/2312.07583 · HTML: https://mlsanity.com/paper/2312.07583/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://mlsanity.com/paper/1610.05755/) (2016)
- [The Composition Theorem for Differential Privacy](https://mlsanity.com/paper/1311.0776/) (2013)
- [Scalable Private Learning with PATE](https://mlsanity.com/paper/1802.08908/) (2018)
- [Deep Learning with Label Differential Privacy](https://mlsanity.com/paper/2102.06062/) (2021)
- [Diffprivlib: The IBM Differential Privacy Library](https://mlsanity.com/paper/1907.02444/) (2019)
- [Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers](https://mlsanity.com/paper/1504.07676/) (2015)
- [Accuracy, Interpretability, and Differential Privacy via Explainable Boosting](https://mlsanity.com/paper/2106.09680/) (2021)
- [Privacy-Preserving Gradient Boosting Decision Trees](https://mlsanity.com/paper/1911.04209/) (2019)
- [Label differential privacy via clustering](https://mlsanity.com/paper/2110.02159/) (2021)
- [Differentially Private Random Decision Forests using Smooth Sensitivity](https://mlsanity.com/paper/1606.03572/) (2016)

## Built on by (most cited first, 4 of 4)

- [Training Differentially Private Ad Prediction Models with Semi-Sensitive Features](https://mlsanity.com/paper/2401.15246/) (2024)
- [Locally Private Estimation with Public Features](https://mlsanity.com/paper/2405.13481/) (2024)
- [On Convex Optimization with Semi-Sensitive Features](https://mlsanity.com/paper/2406.19040/) (2024)
- [A Survey on Differential Privacy for SpatioTemporal Data in Transportation Research](https://mlsanity.com/paper/2407.15868/) (2024)

## Abstract (arXiv)

In this paper, we consider differentially private classification when some features are sensitive, while the rest of the features and the label are not. We adapt the definition of differential privacy naturally to this setting. Our main contribution is a novel adaptation of AdaBoost that is not only provably differentially private, but also significantly outperforms a natural benchmark that assumes the entire data of the individual is sensitive in the experiments. As a surprising observation, we show that boosting randomly generated classifiers suffices to achieve high accuracy. Our approach easily adapts to the classical setting where all the features are sensitive, providing an alternate algorithm for differentially private linear classification with a much simpler privacy proof and comparable or higher accuracy than differentially private logistic regression on real-world datasets.