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Creators/Authors contains: "Wang, Binghui"

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  1. ; ; (, IEEE Transactions on Information Forensics and Security)
    Free, publicly-accessible full text available September 23, 2026
  2. ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    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. 
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    Free, publicly-accessible full text available April 11, 2026
  3. ; ; ; (, Curran Associates)
    Free, publicly-accessible full text available February 21, 2026
  4. ; ; ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    Free, publicly-accessible full text available March 3, 2026
  5. ; (, Springer)
    Free, publicly-accessible full text available December 24, 2025
  6. ; ; ; ; (, ACM)
    Free, publicly-accessible full text available December 2, 2025
  7. ; ; ; ; (, ACM)
    Free, publicly-accessible full text available December 2, 2025
  8. ; ; ; ; (, ACM)
    Full Text Available 
  9. ; ; ; (, USENIX Association)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; ; (, IEEE)
    Full Text Available