论文标题
使用平滑灵敏度差异私人天真贝叶斯分类器
Differentially Private Naive Bayes Classifier using Smooth Sensitivity
论文作者
论文摘要
随着用户数据收集的越来越多，保护个人隐私已引起更多的兴趣。差异隐私是保护个人的强烈概念。幼稚的贝叶斯是流行的机器学习算法之一，用作许多任务的基准。在这项工作中，我们提供了一个私人的幼稚贝叶斯分类器，该分类器与其参数的平滑灵敏度成正比增加了噪声。我们将我们的结果与Vaidya，Shafiq，Basu和Hong进行了比较，其中它们将噪声缩放到参数的全局灵敏度。我们在现实世界数据集上的实验结果表明，我们方法的准确性有了显着提高，同时仍保留$ \ varepsilon $ difference私有。
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.
