Stochastic privacy-preserving methods for nonconvex sparse learning

Sparse learning is essential in mining high-dimensional data. Iterative hard thresholding (IHT) methods are effective for optimizing nonconvex objectives for sparse learning. However, IHT methods are vulnerable to adversary attacks that infer sensitive data. Although pioneering works attempted to re...

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Vydané v:	Information sciences Ročník 630; s. 567 - 585
Hlavní autori:	Liang, Guannan, Tong, Qianqian, Ding, Jiahao, Pan, Miao, Bi, Jinbo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.06.2023
Predmet:	Differential privacy Sparse learning Stochastic algorithm Differential privacy Stochastic algorithm Sparse learning
ISSN:	0020-0255, 1872-6291
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