Parallel distributed Neyman-Pearson detection with privacy constraints

Li, Zuxing; Oechtering, Tobias J.; Jaldén, Joakim

doi:10.1109/iccw.2014.6881292

Cited by 10 publications

(10 citation statements)

References 13 publications

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“…It turns out that the sensor that is being intercepted manipulates its feedback so that the EFC gains practically no information about the true hypothesis, while the LFC still extracts some useful information. The same network scenario was studied assuming Neyman-Pearson detectors in [45], where security was modeled using a constraint placed on the EFC detection probability. Here, it was shown that the optimal local quantizer is a deterministic LRT, while the fusion rule may still be a randomization between two or more LRTs.…”

Section: ) Optimal Quantizationmentioning

confidence: 99%

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

2015

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| The Internet of Things (IoT) will feature pervasive sensing and control capabilities via a massive deployment of machine-type communication (MTC) devices. The limited hardware, low-complexity, and severe energy constraints of MTC devices present unique communication and security challenges. As a result, robust physical-layer security methods that can supplement or even replace lightweight cryptographic protocols are appealing solutions. In this paper, we present an overview of low-complexity physical-layer security schemes that are suitable for the IoT. A local IoT deployment is modeled as a composition of multiple sensor and data subnetworks, with uplink communications from sensors to controllers, and downlink communications from controllers to actuators. The state of the art in physical-layer security for sensor networks is reviewed, followed by an overview of communication network security techniques. We then pinpoint the most energy-efficient and low-complexity security techniques that are best suited for IoT sensing applications. This is followed by a discussion of candidate low-complexity schemes for communication security, such as ON-OFF switching and space-time block codes. The paper concludes by discussing open research issues and avenues for further work, especially the need for a theoretically well-founded and holistic approach for incorporating complexity constraints in physical-layer security designs.

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Section: ) Optimal Quantizationmentioning

confidence: 99%

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

2015

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“…Moreover, the crucial energy efficient issue was not discussed. In [26,27], the optimal local quantizer was examined through minimizing the detection cost at the AFC meanwhile satisfying the constraints to the EFC detection cost or error performance, but the energy consumption problem was not concerned, either. In addition, all of the above solutions were not evaluated over a practical wireless channel and the effect of the transmission channel on their security was not discussed.…”

Section: Related Workmentioning

confidence: 99%

“…For the practical resource constraints and the serious security issues in front of WSN, secure distributed detection schemes under energy constraints are necessary for the development of an efficient IoT. Various secure strategies for distributed detection have been proposed under different assumptions on the eavesdroppers and transmission channels [8,9,10,23,24,25,26,27,28,29]. However, these studies focused on either the local detection at sensors or the information transmission from sensors to the FC.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Sun

2016

Sensors

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We study the secure distributed detection problems under energy constraint for IoT-oriented sensor networks. The conventional channel-aware encryption (CAE) is an efficient physical-layer secure distributed detection scheme in light of its energy efficiency, good scalability and robustness over diverse eavesdropping scenarios. However, in the CAE scheme, it remains an open problem of how to optimize the key thresholds for the estimated channel gain, which are used to determine the sensor’s reporting action. Moreover, the CAE scheme does not jointly consider the accuracy of local detection results in determining whether to stay dormant for a sensor. To solve these problems, we first analyze the error probability and derive the optimal thresholds in the CAE scheme under a specified energy constraint. These results build a convenient mathematic framework for our further innovative design. Under this framework, we propose a hybrid secure distributed detection scheme. Our proposal can satisfy the energy constraint by keeping some sensors inactive according to the local detection confidence level, which is characterized by likelihood ratio. In the meanwhile, the security is guaranteed through randomly flipping the local decisions forwarded to the fusion center based on the channel amplitude. We further optimize the key parameters of our hybrid scheme, including two local decision thresholds and one channel comparison threshold. Performance evaluation results demonstrate that our hybrid scheme outperforms the CAE under stringent energy constraints, especially in the high signal-to-noise ratio scenario, while the security is still assured.

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“…Among them, the problem of Byzantines in the distributed detection network gains more attention recently [19], [20]. In [21], we studied the privacy-constrained parallel distributed Neyman-Pearson detection problem where the detection performance and eavesdropping privacy risk are evaluated by the Neyman-Pearson detection-operational metrics. For an optimal privacy-constrained distributed Neyman-Pearson detection design, it is sufficient to consider deterministic LRTs for the remote decision makers while a randomized fusion strategy might be needed.…”

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confidence: 99%