Distributed inference in the presence of eavesdroppers: a survey

Abstract

The distributed inference framework comprises of a group of spatially distributed nodes which acquire observations about a phenomenon of interest. Due to bandwidth and energy constraints, the nodes often quantize their observations into a finite-bit local message before sending it to the fusion center (FC). Based on the local summary statistics transmitted by nodes, the FC makes a global decision about the presence of the phenomenon of interest. The distributed and broadcast nature of such systems makes them quite vulnerable to different types of attacks. This paper addresses the problem of secure communication in the presence of eavesdroppers. In particular, we focus on efficient mitigation schemes to mitigate the impact of eavesdropping. We present an overview of the distributed inference schemes under secrecy constraints and describe the currently available approaches in the context of distributed detection and estimation followed by a discussion on avenues for future research.

Physical-Layer Security in the Internet of Things: Sensing and Communication Confidentiality Under Resource Constraints

“…Most distributed detection problems in sensor networks involve a fusion center trying to infer the state of a target phenomenon based on reports from noncolocated sensors [33], [34]. Security issues arise when an LFC would like to prevent an EFC from performing the same inference [35]. A binary hypothesis test is the simplest such case, where the unknown state H belongs to one of two possible hypotheses: 2 f 0 ; 1 g. The LFC performs data fusion if the sensors report their (quantized) observations, or decision fusion if the sensors report their individual decisions (for, e.g., 1-bit feedback to indicate the local state estimate).…”

Optimal Parameter Encoding Based on Worst Case Fisher Information Under a Secrecy Constraint

“…The estimation theoretic secrecy is also employed in distributed inference networks, where the information coming to a fusion center from various sensor nodes can also be observed by eavesdroppers [15]. In estimation theoretic approaches, the Cramér-Rao bounds (CRBs) provide useful fundamental limits for assessing performance of estimators, hence they can be employed as a performance metric for the intended receiver to optimize [16], [19].…”

Secure Distributed Detection under Energy Constraint in IoT-Oriented Sensor Networks

“…However, the sensors’ transmissions are overheard by the EFC, who also wishes to detect the target state. From the literature [2,7,9], we have seen that the stochastic ciphering could be employed to protect the information of the sensors from the EFC efficiently, since each sensor would flip its decision randomly and the EFC would be confused when it was ignorant about the flipping probability (i.e., the encryption key). However, the key exchange between the AFC and the sensor itself may be not secure from the EFC.…”

“…Aiming to the spectrum scarcity problem, some enhanced technologies with high spectrum efficiency are advocated, for example, the cognitive Internet of Things (CIoT) who introduces the cognitive radio technology to the IoT network [5]. A decentralized inference network where the nodes transmit the compressed observations to reduce the required bandwidth is another solution [7], and the distributed detection technique utilized in sensor networks is a typical instance [8,9,10,11]. Since a huge number of devices are included in IoT, the energy to be spent for communication and computation is extremely large and improving energy efficiency becomes more important.…”