Differentially Private Naïve Bayes Classifier Using Smooth Sensitivity
Farzad Zafarani, Clifton Chris · DOAJ (DOAJ: Directory of Open Access Journals) · 2021
There is increasing awareness of the need to protect individual privacy in the training data used to develop machine learning models. Differential Privacy is a strong concept of protecting individuals. Naïve Bayes is a popular machine learning algorithm, used as a baseline for many tasks. In this work, we have provided a differentially private Naïve Bayes classifier that adds noise proportional to the smooth sensitivity of its parameters. We compare our results to Vaidya, Shafiq, Basu, and Hong [1] which scales noise to the global sensitivity of the parameters. Our experimental results on real-world datasets show that smooth sensitivity significantly improves accuracy while still guaranteeing ɛ-differential privacy.