2020/03/31 by Farzad Zafarani, Zafarani, Farzad, Chris Clifton +1
Computer Science · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.CR #cs.LG
paper · pdf · doi:10.48550/arxiv.2003.13955
14 Pages, PETS'21
arxiv created 2021/07/19 · arxiv updated 2021/07/20
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.