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A Review on Penetration Testing for Privacy of Deep Learning Models

Salma Akther Wencheng Yang Song Wang Shicheng Wei Ji Zhang Xu Yang Yanrong Lu Yan Li

Computers, Materials & Continua2026，Vol.87Issue(5)：P.43-76,34.

Computers, Materials & Continua2026，Vol.87Issue(5)：P.43-76,34.DOI:10.32604/cmc.2026.076358

A Review on Penetration Testing for Privacy of Deep Learning Models

Salma Akther ¹Wencheng Yang ¹Song Wang ²Shicheng Wei ¹Ji Zhang ¹Xu Yang ³Yanrong Lu ⁴Yan Li¹

作者信息

1. School of Mathematics,Physics and Computing,University of Southern Queensland,Toowoomba,QLD,Australia
2. Department of Engineering,La Trobe University,Melbourne,VIC,Australia
3. School of Computer and Data Science,Minjiang University,Fuzhou,China
4. School of Security Science and Engineering,Civil Aviation University of China,Tianjin,China
折叠

摘要

关键词

Penetration testing/deep learning/homomorphic encryption/differential privacy/federated learning

分类

信息技术与安全科学

引用本文复制引用

Salma Akther,Wencheng Yang,Song Wang,Shicheng Wei,Ji Zhang,Xu Yang,Yanrong Lu,Yan Li..A Review on Penetration Testing for Privacy of Deep Learning Models[J].Computers, Materials & Continua,2026,87(5):P.43-76,34.

基金项目

supported in part by the Tianjin Natural Science Foundation Project(24JCZDJC01000) （24JCZDJC01000）

the Fundamental Research Funds for the Central Universities of China(No.3122025091). （No.3122025091）

Computers, Materials & Continua

ISSN：1546-2218

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