Mario Diaz

Information Extraction Under Privacy Constraints

A privacy-constrained information extraction problem is considered where for a pair of correlated discrete random variables

On the Capacity of Block Multiantenna Channels

(X,Y)

On the symmetries and the capacity achieving input covariance matrices of multiantenna channels

governed by a given joint distribution, an agent observes

Privacy-aware guessing efficiency

Y

Estimation Efficiency Under Privacy Constraints

and wants to convey to a potentially public user as much information about

On the Global Fluctuations of Block Gaussian Matrices

Y

Random Matrix Systems with Block-Based Behavior and Operator-Valued Models

as possible without compromising the amount of information revealed about

On the Noise-Information Separation of a Private Principal Component Analysis Scheme.

X

On the Contractivity of Privacy Mechanisms.

. To this end, the so-called {\em rate-privacy function} is introduced to quantify the maximal amount of information (measured in terms of mutual information) that can be extracted from

On Random Operator-Valued Matrices: Operator-Valued Semicircular Mixtures and Central Limit Theorem

Y