Author

Hanlin Gu

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PrivSynth: Alternating and Control-Based Optimization for Privacy and Utility in Synthetic Data

PrivSynth is proposed, a framework that quantiﬁes multiple privacy risks and integrates it into the control ob-jective, and achieves better utility and stronger privacy protection than state-of-the-art methods.

Xinyuan Zhao, Hanlin Gu, Gui-Bao Song et al. · 0 citations

Aug 2026

FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation.

This article proposes a novel adaptive obfuscation mechanism, coined FedAdOb, to protect private data without yielding original model performances, and utilizes passport-based adaptive obfuscation to ensure data privacy in both horizontal and vertical federated learning settings.

Hanlin Gu, Jia-Huan Luo, Yan Kang et al. · IEEE Transactions on Pattern... · 0 citations