Sanket Satpathy

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We study secure source-coding with causal disclosure, under the Gaussian distribution. The optimality of Gaussian auxiliary random variables is shown in various scenarios. We explicitly characterize the tradeoff between the rates of communication and secret key. This tradeoff is the result of a mutual information optimization under Markov constraints. As a(More)
We consider the problem of generating correlated random variables in a distributed fashion, where communication is constrained to a cascade network. The first node in the cascade observes an i.i.d. sequence <inline-formula> <tex-math notation="LaTeX">$X^{n}$ </tex-math></inline-formula> locally before initiating communication along the cascade. All nodes(More)
We investigate the problem of secure source coding with a two-sided helper in a game-theoretic framework. Alice (A) and Helen (H) view iid correlated information sequences X<sup>n</sup> and Y<sup>n</sup> respectively. Alice communicates to Bob (B) at rate R, while H broadcasts a message to both A and B at rate R<sub>H</sub>. Additionally, A and B share(More)
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