Differentially-private (DP) mechanisms can be embedded into the design of a machine learningalgorithm to protect the resulting model against privacy leakage, although this often comes with asignificant loss of accuracy. In this paper, we aim at improving this trade-off for rule lists modelsby establishing the smooth sensitivity of the Gini impurity and leveraging it to propose a DP greedyrule list algorithm. In particular, our theoretical analysis and experimental results demonstrate thatthe DP rule lists models integrating smooth sensitivity have higher accuracy that those using otherDP frameworks based on global sensitivity.

翻译：差分隐私（DP）机制可嵌入机器学习算法的设计中，以保护最终模型免受隐私泄露，但这往往会导致显著的精度损失。本文旨在通过建立基尼不纯度的平滑灵敏度，并利用该理论提出一种差分隐私贪心规则列表算法，从而改善规则列表模型中隐私保护与精度之间的权衡。我们的理论分析与实验结果表明，与基于全局灵敏度的其他差分隐私框架相比，采用平滑灵敏度的差分隐私规则列表模型具有更高的精度。