A Comparative Analysis of Defense Mechanisms Against Model Inversion Attacks on Tabular Data

As more machine learning models are used in sensitive fields like healthcare, finance, and smart infrastructure, protecting structured tabular data from privacy attacks is a key research challenge. Although several privacy-preserving methods have been proposed for tabular data, a comprehensive compa...

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書誌詳細

主要な著者:	Neethu Vijayan, Raj Gururajan, Ka Ching Chan
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2025-10-01
シリーズ:	Journal of Cybersecurity and Privacy
主題:	differential privacy federated learning model inversion attack privacy-preserving machine learning tabular data security
オンライン･アクセス:	https://www.mdpi.com/2624-800X/5/4/80
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