Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists

arXiv:2403.13848v1 Announce Type: cross
Abstract: 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 …

accuracy aim arxiv can cs.ai cs.cr cs.lg design embedded lists loss machine privacy private protect trade