Jing Deng

Ali Moharrer, Shuangqing Wei, George T. Amariucai et al.

In latent Gaussian trees the pairwise correlation signs between the variables are intrinsically unrecoverable. Such information is vital since it completely determines the direction in which two variables are associated. In this work, we resort to information theoretical approaches to achieve two fundamental goals: First, we quantify the amount of information loss due to unrecoverable sign information. Second, we show the importance of such information in determining the maximum achievable rate region, in which the observed output vector can be synthesized, given its probability density function. In particular, we model the graphical model as a communication channel and propose a new layered encoding framework to synthesize observed data using upper layer Gaussian inputs and independent Bernoulli correlation sign inputs from each layer. We find the achievable rate region for the rate tuples of multi-layer latent Gaussian messages to synthesize the desired observables.

Mohammad Reza Khalili Shoja, George Traian Amariucai, Shuangqing Wei et al.

Establishing secret common randomness between two or multiple devices in a network resides at the root of communication security. The problem is traditionally decomposed into a randomness generation stage (randomness purity is subject to employing often costly true random number generators) and a key-agreement information exchange stage, which can rely on public-key infrastructure or on key wrapping. In this paper, we propose KERMAN, an alternative key establishment algorithm for ad-hoc networks which works by harvesting randomness directly from the network routing metadata, thus achieving both pure randomness generation and (implicitly) secret-key agreement. Our algorithm relies on the route discovery phase of an ad-hoc network employing the Dynamic Source Routing protocol, is lightweight, and requires minimal communication overhead.