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Binghui Zhang

dblp:371/9175 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Security and privacy · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
2 papers
Privacy and data protection · 94% Security and privacy of machine learning · 6%
Artificial intelligence
1 paper
Trustworthy machine learning · 77% Representation and self-supervised learning · 23%

Topics — the 8 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning › robustness
adversarial robustness		 0.912025
Learning Robust and Privacy-Preserving Representations via Information Theory · AAAI 2025
Privacy and data protection › sensitive data protection
attribute privacy		 0.912025
Learning Robust and Privacy-Preserving Representations via Information Theory · AAAI 2025
Privacy and data protection › privacy metrics › privacy quantification
information-theoretic privacy		 0.912025
Learning Robust and Privacy-Preserving Representations via Information Theory · AAAI 2025
Privacy and data protection › privacy protection mechanisms
inference attack defense		 0.812024
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks · USENIX Security Symposium 2024
Privacy and data protection › privacy-preserving machine learning
privacy-preserving representation learning		 0.812024
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks · USENIX Security Symposium 2024
Machine learning › Representation and self-supervised learning › representation learning
robust representation learning		 0.312025
Learning Robust and Privacy-Preserving Representations via Information Theory · AAAI 2025
Privacy and data protection
inference attack		 0.212024
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks · USENIX Security Symposium 2024
Security and privacy of machine learning
privacy attack		 0.212024
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks · USENIX Security Symposium 2024

Methods — techniques the papers use, named apart from their topics

representation learning · 1.7information theory · 1.7information-theoretic framework · 0.8
YearPublicationVenuePosition
2025 Learning Robust and Privacy-Preserving Representations via Information Theory
abstract
Machine learning models are vulnerable to both security attacks (e.g., adversarial examples) and privacy attacks (e.g., private attribute inference). We take the first step to mitigate both the security and privacy attacks, and maintain task utility as well. Particularly, we propose an information-theoretic framework to achieve the goals through the lens of representation learning, i.e., learning representations that are robust to both adversarial examples and attribute inference adversaries. We also derive novel theoretical results under our framework, e.g., the inherent trade-off between adversarial robustness/utility and attribute privacy, and guaranteed attribute privacy leakage against attribute inference adversaries.
Binghui Zhang, Sayedeh Leila Noorbakhsh, Yuan Hong 0001, Binghui Wang
 AAAI1
2024 Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks
Sayedeh Leila Noorbakhsh, Binghui Zhang, Yuan Hong 0001, Binghui Wang
 USENIX Security Symposium2