Secrecy Constrained Distributed Detection in Sensor Networks

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The data collected by sensor networks often contain sensitive information and care must be taken to prevent that information from being leaked to malicious third parties, e.g., eavesdroppers. Under both the Neyman–Pearson and Bayesian frameworks, we investigate the strategy of defending against an informed and greedy eavesdropper who has access to all the sensors’ outputs via imperfect communication channels. Meanwhile, the legitimate user, e.g., fusion center, is guaranteed to achieve its desired detection performance. Under the Neyman–Pearson framework, we propose a novel approach for analyzing the performance tradeoff, using the maximum achievable detection performance ratio between the fusion center and eavesdropper. Under the Bayesian framework, we derive the asymptotic error exponent, and show that the detectability of a given eavesdropper (Eve) can be limited to her prior information, in other words, Eve’s observations do not improve her decision-making ability. Furthermore, we show that as the number of sensors goes to infinity, both asymptotic perfect secrecy and asymptotic perfect detection can be achieved under both frameworks, given noiseless communication channels to the fusion center.