Design of Binary Quantizers for Distributed Detection under Secrecy Constraints
This addresses security in distributed sensor networks for applications like surveillance or IoT, but it is incremental as it builds on existing detection theory with secrecy constraints.
The paper tackles the design of binary quantizers for distributed detection networks to maximize detection performance at the fusion center while limiting information leakage to an eavesdropper, proving that optimal quantizers use a likelihood ratio test and providing algorithms for i.i.d. and non-i.i.d. cases with numerical performance illustrations.
In this paper, we investigate the design of distributed detection networks in the presence of an eavesdropper (Eve). We consider the problem of designing binary quantizers at the sensors that maximize the Kullback-Leibler (KL) Divergence at the fusion center (FC), subject to a tolerable constraint on the KL Divergence at Eve. In the case of i.i.d. received symbols at both the FC and Eve, we prove that the structure of the optimal binary quantizers is a likelihood ratio test (LRT). We also present an algorithm to find the threshold of the optimal LRT, and illustrate it for the case of Additive White Gaussian Noise (AWGN) observation models at the sensors. In the case of non-i.i.d. received symbols at both FC and Eve, we propose a dynamic-programming based algorithm to find efficient quantizers at the sensors. Numerical results are presented to illustrate the performance of the proposed network design.