  A principled procedure to infer a hierarchy of statistical distributions
possessing ill-conditioned eigenstructures, from incomplete constraints, is
presented. The inference process of the \textit{pdf}'s employs the Fisher
information as the measure of uncertainty, and, utilizes a semi-supervised
learning paradigm based on a measurement-response model. The principle
underlying the learning paradigm involves providing a quantum mechanical
connotation to statistical processes. The inferred \textit{pdf}'s constitute a
statistical host that facilitates the encryption/decryption of covert
information (code). A systematic strategy to encrypt/decrypt code via unitary
projections into the \textit{null spaces} of the ill-conditioned
eigenstructures, is presented. Numerical simulations exemplify the efficacy of
the model.
