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- W3184057774 abstract "Abstract 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." @default.
- W3184057774 created "2021-08-02" @default.
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- W3184057774 date "2021-07-23" @default.
- W3184057774 modified "2023-10-16" @default.
- W3184057774 title "Differentially Private Naïve Bayes Classifier Using Smooth Sensitivity" @default.
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- W3184057774 doi "https://doi.org/10.2478/popets-2021-0077" @default.
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