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We introduce Network Maximal Correlation (NMC) as a multivariate measure of nonlinear association among random variables. NMC is defined via an optimization that infers transformations of variables by maximizing aggregate inner products between transformed variables. For finite discrete and jointly Gaussian random variables, we characterize a solution of the NMC optimization using basis expansion...
Consider a multi-source multicast network coding problem with correlated sources. While the fundamental limits are known, achieving them, in general, involves a computational burden due to the complex decoding process. Efficient solutions, on the other hand, are by large based on source and network coding separation, thus imposing strict topological constraints on the networks which can be solved...
A novel method of data masking is presented which allows Poisson and some Gaussian data sets to be altered without changing the overall probability density function. The method consists of multiplying each entry by a random complex exponential function, i.e. a rotation in the complex plane, and exchanging imaginary components between entries. The transformation preserves the properties of the data...
We consider the problem of diluting common randomness from correlated observations by separated agents. This problem creates a new framework to study statistical privacy, in which a legitimate party, Alice, has access to a random variable X, whereas an attacker, Bob, has access to a random variable Y dependent on X drawn from a joint distribution pX,Y. Alice's goal is to produce a non-trivial function...
We investigate the problem of intentionally disclosing information about a set of measurement points X (useful information), while guaranteeing that little or no information is revealed about a private variable S (private information). Given that S and X are drawn from a finite set with joint distribution pS,X, we prove that a non-trivial amount of useful information can be disclosed while not disclosing...
Due to its wide availability narrowband powerline networks provide an interesting no-new-wires communication channel. Nevertheless, as powerline was not designed for data communication, its electrical characteristics make it a harsh environment for data transmission and prevents the deployment of services with high reliability requirements. This paper presents 3 main outcomes: (i) The characterization...
Lower bounds for the average probability of error of estimating a hidden variable X given an observation of a correlated random variable Y, and Fano's inequality in particular, play a central role in information theory. In this paper, we present a lower bound for the average estimation error based on the marginal distribution of X and the principal inertias of the joint distribution matrix of X and...
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