The capacity for multiterminal secret-key agreement inspires a natural generalization of Shannon's mutual information from two random variables to multiple random variables. Under a general source model without helpers, the capacity is shown to be equal to the normalized divergence from the joint distribution of the random sources to the product of marginal distributions minimized over partitions of the random sources. The mathematical underpinnings are the works on co-intersecting submodular functions and the principle lattices of partitions of the Dilworth truncation. We clarify the connection to these works and enrich them with information-theoretic interpretations and properties that are useful in solving other related problems in information theory as well as machine learning.

Alice wishes to potentially communicate with Bob over a compound Binary Symmetric Channel while Willie listens in over a compound Binary Symmetric Channel that is noisier than Bob's. The channel noise parameters for both Bob and Willie are drawn according to uniform distribution over a range, but none of the three parties know their exact values. Willie's goal is to infer whether or not Alice is communicating with Bob. We show that Alice can send her messages reliably to Bob while ensuring that even whether or not she is actively communicating is deniable to Willie. We find the best rate at which Alice can communicate both deniably and reliably using Shannon's random coding and prove a converse.

We formulate an info-clustering paradigm based on a multivariate information measure, called multivariate mutual information, that naturally extends Shannon’s mutual information between two random variables to the multivariate case involving more than two random variables. With proper model reductions, we show that the paradigm can be applied to study the human genome and connectome in a more meaningful way than the conventional algorithmic approach. Not only can info-clustering provide justifications and refinements to some existing techniques, but it also inspires new computationally feasible solutions.

A mutual dependence expression is established for the secret key agreement problem when all users are active. In certain source networks, the expression can be interpreted as certain notions of connectivity and network information flow. In particular, the secrecy problem can be mapped to a new class of network coding problems with selectable links and undirected broadcast links. For such networks, the secrecy capacities serve as upper bounds on the maximum network throughputs, while the network coding solutions can be used for secret key agreement.

For the multiterminal secret key agreement problem under a private source model, it is known that the maximum key rate, i.e., the secrecy capacity, can be achieved through communication for omniscience, but the omniscience strategy can be strictly suboptimal in terms of minimizing the public discussion rate. While a single-letter characterization is not known for the minimum discussion rate needed for achieving the secrecy capacity, we derive single-letter lower bounds that yield some simple conditions for omniscience to be discussion-rate optimal. These conditions turn out to be enough to deduce the optimality of omniscience for a large class of sources, including the hypergraphical sources. We also extend our results to more general class of multiterminal sources with helpers and silent users.

Modulation classification is crucial in applications such as electronic warfare and interference cancellation. In this letter, a novel feature-based Kolmogorov-Smirnov classifier is proposed for the identification of the modulation formats. The received signal is first preprocessed with a folding operation that helps identify the modulation formats based on their different axes of symmetry. Simulation results show that the performance of the proposed classifier is close to that of the optimal likelihood-based classifier, while its robustness to noise uncertainty is improved and its computational complexity is reduced compared to that of the optimal likelihood-based classifier.