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Smooth Sensitivity for Learning Differentially-Private yet Accurate Rule Lists
March 22, 2024, 4:41 a.m. | Timoth\'ee Ly (LAAS-ROC), Julien Ferry (EPM), Marie-Jos\'e Huguet (LAAS-ROC), S\'ebastien Gambs (UQAM), Ulrich Aivodji (ETS)
cs.LG updates on arXiv.org arxiv.org
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 …
abstract accuracy aim arxiv cs.ai cs.cr cs.lg design embedded improving lists loss machine paper privacy protect sensitivity trade trade-off type
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