Secure distributed detection in the presence of eavesdroppers

Nadendla, Venkata Sriram Siddhardh; Chen, Hao; Varshney, Pramod K.

doi:10.1109/acssc.2010.5757773

Cited by 20 publications

(24 citation statements)

References 6 publications

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“…The first two terms in (8) are constants and lead to the upper bound of the privacy metric c min E ≤ min{c 1 E , c 2 E }.…”

Section: A Privacy Metricmentioning

confidence: 99%

“…In [8], the privacy risk is measured in the information theoretic perspective. In this work, we propose a novel detection operational privacy metric.…”

Section: Privacy In Distributed Bayesian Detectionmentioning

confidence: 99%

“…Some other studies investigated routing security technologies in multi-hop sensor networks, e.g., the reputation-based scheme [5] and broadcast authentication [6]. Recently, a novel idea has been proposed in the pioneering works [7], [8] to consider the privacy issue of sensor networks in the physical-layer distributed detection design. In [7], the eavesdropper was assumed to be interested in the data transmission state of the system.…”

Section: Introductionmentioning

confidence: 99%

“…However, an eavesdropper in practice can be more aggressive. In [8], the privacy risk was assessed by an entropy-based metric measuring the difference between Kullback-Leibler distances of the fusion node and eavesdropper.…”

Section: Introductionmentioning

confidence: 99%

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Parallel distributed Bayesian detection with privacy constraints

Oechtering

Kittichokechai

2014

2014 IEEE International Conference on Communications (ICC)

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In this paper, the privacy problem of a parallel distributed detection system vulnerable to an eavesdropper is proposed and studied in the Bayesian formulation. The privacy risk is evaluated by the detection cost of the eavesdropper which is assumed to be informed and greedy. It is shown that the optimal detection strategy of the sensor whose decision is eavesdropped on is a likelihood-ratio test. This fundamental insight allows for the optimization to reuse known algorithms extended to incorporate the privacy constraint. The trade-off between the detection performance and privacy risk is illustrated in a numerical example. The incorporation of physical layer privacy in the system design will lead to trustworthy sensor networks in future.

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“…The first two terms in (8) are constants and lead to the upper bound of the privacy metric c min E ≤ min{c 1 E , c 2 E }.…”

Section: A Privacy Metricmentioning

confidence: 99%

“…In [8], the privacy risk is measured in the information theoretic perspective. In this work, we propose a novel detection operational privacy metric.…”

Section: Privacy In Distributed Bayesian Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parallel distributed Bayesian detection with privacy constraints

Oechtering

Kittichokechai

2014

2014 IEEE International Conference on Communications (ICC)

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“…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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