ECRB-Based Optimal Parameter Encoding Under Secrecy Constraints - 2018


In this Project, optimal deterministic encoding of a scalar parameter is investigated in the presence of an eavesdropper. The aim is to minimize the expectation of the conditional Cramér-Rao bound at the supposed receiver while keeping the mean-squared error (MSE) at the eavesdropper on top of a sure threshold. Initial, optimal encoding functions are derived within the absence of secrecy constraints for any given prior distribution on the parameter. Next, an optimization problem is formulated below a secrecy constraint and numerous answer approaches are proposed. Conjointly, theoretical results on the shape of the optimal encoding operate are provided beneath the idea that the eavesdropper employs a linear minimum mean-squared error (MMSE) estimator. Numerical examples are presented to illustrate the theoretical results and to investigate the performance of the proposed resolution approaches.

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