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Learning an Adversarys Actions for Secret Communication

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 Added by Mehrdad Tahmasbi
 Publication date 2018
and research's language is English




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Secure communication over a wiretap channel is investigated, in which an active adversary modifies the state of the channel and the legitimate transmitter has the opportunity to sense and learn the adversarys actions. The adversary has the ability to switch the channel state and observe the corresponding output at every channel use while the encoder has causal access to observations that depend on the adversarys actions. A joint learning/transmission scheme is developed in which the legitimate users learn and adapt to the adversarys actions. For some channel models, it is shown that the achievable rates, defined precisely for the problem, are arbitrarily close to those obtained with hindsight, had the transmitter known the actions ahead of time. This initial study suggests that there is much to exploit and gain in physical-layer security by learning the adversary, e.g., monitoring the environment.



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Secure communication with feedback is studied. An achievability scheme in which the backward channel is used to generate a shared secret key is proposed. The scenario of binary symmetric forward and backward channels is considered, and a combination of the proposed scheme and Maurers coding scheme is shown to achieve improved secrecy rates. The scenario of a Gaussian channel with perfect output feedback is also analyzed and the Schalkwijk-Kailath coding scheme is shown to achieve the secrecy capacity for this channel.
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