Differentially Private Naive Bayes Classifier using Smooth Sensitivity

Authors: Farzad Zafarani, Chris Clifton
Published: 2020-03-31 | Updated: 2021-07-19

Source: https://arxiv.org/abs/2003.13955

PDF: https://arxiv.org/pdf/2003.13955

Labels Predicted by AI

プライバシー保護手法 計算効率 機械学習の応用

Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.

Abstract

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 ε-differential privacy.