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Creators/Authors contains: "Vu, Minh N"

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  1. ; ; ; ; (, IEEE transactions on artificial intelligence)
    Free, publicly-accessible full text available March 3, 2026
  2. ; ; ; (, EAI International Conference on Security and Privacy in Communication Networks (SecureComm))
    Accepted Manuscript Full Text Available
  3. ; ; ; ; (, IEEE Transactions on Artificial Intelligence)
    Free, publicly-accessible full text available October 1, 2026
  4. ; (, IEEE)
    Full Text Available 
  5. ; ; ; (, IEEE International Conference on Big Data)
    Full Text Available 
  6. ; ; (, IEEE International Conference on Communications)
    null (Ed.)
    Full Text Available 
  7. ; ; ; ; ; ; (, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence)
    In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM) to preserve differential privacy in deep neural networks, with provable robustness against adversarial examples. We first relax the constraint of the privacy budget in the traditional Gaussian Mechanism from (0, 1] to (0, infty), with a new bound of the noise scale to preserve differential privacy. The noise in our mechanism can be arbitrarily redistributed, offering a distinctive ability to address the trade-off between model utility and privacy loss. To derive provable robustness, our HGM is applied to inject Gaussian noise into the first hidden layer. Then, a tighter robustness bound is proposed. Theoretical analysis and thorough evaluations show that our mechanism notably improves the robustness of differentially private deep neural networks, compared with baseline approaches, under a variety of model attacks. 
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