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Creators/Authors contains: "Liao, Jiachun"

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  1. ; ; ; (, International Symposium on Information Theory)
    null (Ed.)
    The minimum mean-square error (MMSE) achievable by optimal estimation of a random variable S given another random variable T is of much interest in a variety of statistical contexts. Motivated by a growing interest in auditing machine learning models for unintended information leakage, we propose a neural network-based estimator of this MMSE. We derive a lower bound for the MMSE based on the proposed estimator and the Barron constant associated with the conditional expectation of S given T . Since the latter is typically unknown in practice, we derive a general bound for the Barron constant that produces order optimal estimates for canonical distribution models. 
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  2. ; ; ; ; ; (, IEEE Transactions on Information Forensics and Security)
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  3. ; ; ; ; (, IEEE Journal on Selected Areas in Information Theory)
    null (Ed.)
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  4. ; ; ; (, 2020 IEEE Conference on Communications and Network Security (CNS))
    null (Ed.)
    Maximal α-leakage is a tunable measure of information leakage based on the quality of an adversary's belief about an arbitrary function of private data based on public data. The parameter α determines the loss function used to measure the quality of a belief, ranging from log-loss at α = 1 to the probability of error at α = ∞. We review its definition and main properties, including extensions to α <; 1, robustness to side information, and relationship to Rényi differential privacy. 
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  5. ; ; ; ; (, Proc. of the 2020 IEEE International Symposium on Information Theory (ISIT))
    null (Ed.)
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  6. ; ; ; (, IEEE Transactions on Information Theory)
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  7. ; ; ; (, International Symposium on Information Theory)
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