
Strong Converse for Hypothesis Testing Against Independence Over A Noisy Channel
We revisit the hypothesis testing problem against independence over a no...
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Distributed Hypothesis Testing Under Privacy Constraints
A distributed binary hypothesis testing problem involving two parties, a...
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Strong Converse for Hypothesis Testing Against Independence over a TwoHop Network
By proving a strong converse, we strengthen the weak converse result by ...
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Limits of Deepfake Detection: A Robust Estimation Viewpoint
Deepfake detection is formulated as a hypothesis testing problem to clas...
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Strong Converse for Testing Against Independence over a Noisy channel
A distributed binary hypothesis testing (HT) problem over a noisy channe...
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Smart Meter Privacy: Adversarial Hypothesis Testing Models
Smart meter privacy and privacypreserving energy management are studied...
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Testing for Anomalies: Active Strategies and Nonasymptotic Analysis
The problem of verifying whether a multicomponent system has anomalies ...
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Hypothesis Testing with Privacy Constraints Over A Noisy Channel
We consider a hypothesis testing problem with privacy constraints over a noisy channel and derive fundamental limits of optimal tests under the NeymanPearson criterion. The fundamental limit of interest is the privacyutility tradeoff (PUT) between the exponent of the typeII error probability and the leakage of the information source subject to a constant constraint on the typeI error probability. We provide exact characterization of the asymptotic PUT for any nonvanishing typeI error probability. In particular, we show that tolerating a larger typeI error probability cannot increase the PUT. Such a result is known as strong converse or strong impossibility theorem. To prove the strong converse theorem, we apply the recently proposed strong converse technique by Tyagi and Watanabe (TIT 2020) and further demonstrate the generality of the technique. The strong converse theorems for several problems, such as hypothesis testing against independence over a noisy channel (Sreekumar and Gündüz, TIT 2020) and hypothesis testing with communication and privacy constraints (Gilani et al., Entropy 2020), are established or recovered as special cases of our result.
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