Optimal Power Allocation for Secure Estimation of Multiple Parameters

Optimal power allocation for secure estimation of multiple deterministic parameters is investigated under a total constraint. The goal to minimize the Cramer-Rao lower bound (CRLB) at an intended receiver while keeping errors eavesdropper above specified target levels. To that end, optimization problem formulated by considering measurement models involving linear transformation parameter vector and additive Gaussian noise. Although proposed nonconvex, it decomposed into convex sub-problems utilizing structure secrecy constraints. Then, optimal solutions are characterized via theoretic approaches. An algorithm characterization developed obtain solution problem.