Design of Binary Quantizers for Distributed Detection under Secrecy Constraints
Abstract
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 sensor quantizers that maximize the Kullback-Leibler (KL) divergence at the fusion center (FC), when subject to a tolerable constraint on the KL divergence at Eve. We assume that the channels between the sensors and the FC (likewise the channels between the sensors and the Eve) are modeled as binary symmetric channels (BSCs). 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.
Recommended Citation
V. S. Nadendla and P. K. Varshney, "Design of Binary Quantizers for Distributed Detection under Secrecy Constraints," IEEE Transactions on Signal Processing, vol. 64, no. 10, pp. 2636 - 2648, Institute of Electrical and Electronics Engineers (IEEE), May 2016.
The definitive version is available at https://doi.org/10.1109/TSP.2016.2529583
Department(s)
Computer Science
Keywords and Phrases
Distributed Detection; Eavesdroppers; Kullback-Leibler Divergence; Secrecy; Wireless Sensor Networks
International Standard Serial Number (ISSN)
1053-587X; 1941-0476
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 May 2016
Comments
This work was supported, in part by the ARO under Grant W911NF-14-1-0339 and the Center for Advanced Systems and Engineering (CASE) at Syracuse University.