# Differentially Private Naive Bayes Classifier using Smooth Sensitivity

arXiv: https://arxiv.org/abs/2003.13955 · HTML: https://mlsanity.com/paper/2003.13955/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning](https://mlsanity.com/paper/2410.12085/) (2024)
- [Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists](https://mlsanity.com/paper/2403.13848/) (2024)

## Abstract (arXiv)

With the increasing collection of users' data, protecting individual privacy has gained more interest. Differential Privacy is a strong concept of protecting individuals. Naive Bayes is one of the popular machine learning algorithm, used as a baseline for many tasks. In this work, we have provided a differentially private Naive Bayes classifier that adds noise proportional to the Smooth Sensitivity of its parameters. We have compared our result to Vaidya, Shafiq, Basu, and Hong in which they have scaled the noise to the global sensitivity of the parameters. Our experiment results on the real-world datasets show that the accuracy of our method has improved significantly while still preserving varepsilon-differential privacy.