Full Citation

Title: Differentially Private Naive Bayes Classifier using Smooth Sensitivity

Citation Type: Miscellaneous

Publication Year: 2020

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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 [19] 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.

Url: http://arxiv.org/abs/2003.13955

User Submitted?: No

Authors: Zafarani, Farzad; Clifton, Chris

Publisher: Department of Computer Science, Purdue University

Data Collections: IPUMS USA

Topics: Other, Population Data Science

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