Secure Distributed Detection in the Presence of Eavesdroppers
We investigate the structure of quantizer rules at the local sensors in distributed detection networks, in the presence of eavesdroppers (Eve), under asymptotic regime (number of sensors tending to infinity) for binary hypotheses. These local quantizers are designed in such a way that the confidentiality of sensor data is preserved while achieving optimal detection performance at the fusion center (FC). In the case of Eve with noisier channels, for a general channel model, we show that these optimal quantizer rules at the local sensors are always on the boundaries of the achievable region of sensor's ROC. If there is a constraint on the Eve's performance, based on our numerical results, we conjecture that the structure of an optimal quantizer is LRT-based. The above argument is corroborated with a numerical example using BSC channels for the Eve and ideal channels for the FC. In the case of Eve with better channels, we prove that the quantizer rules that can provide confidentiality along with optimal detection performance, cannot send any useful information to the fusion center (FC). We propose a jamming scheme for the FC against Eve and evaluate the optimal distribution for the Gaussian jamming signal that requires minimum energy to make both FC and Eve's channel similar in distributed detection performance.
V. S. Nadendla et al., "Secure Distributed Detection in the Presence of Eavesdroppers," Proceedings of the 44th Asilomar Conference on Signals, Systems and Computers (2010, Pacific Grove, CA), pp. 1437-1441, Institute of Electrical and Electronics Engineers (IEEE), Nov 2010.
The definitive version is available at https://doi.org/10.1109/ACSSC.2010.5757773
44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 (2010: Nov. 7-10, Pacific Grove, CA)
Keywords and Phrases
Achievable Region; Asymptotic Regimes; Channel Model; Distributed Detection; Fusion Center; Gaussians; Jamming Signals; Local Sensors; Minimum Energy; Numerical Example; Numerical Results; Optimal Detection; Optimal Distributions; Quantizers; Sensor Data, Jamming; Sensor Data Fusion; Sensors; Structural Optimization, Signal Detection
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Article - Conference proceedings
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01 Nov 2010