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.

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