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Parallel Distributed Bayesian Detection with Privacy Constraints
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-2276-2079
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-0036-9049
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2014 (English)In: Proceedings of the IEEE International Conference on Communications (ICC) 2014, 2014, 2178-2183 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2014. 2178-2183 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-168111DOI: 10.1109/ICC.2014.6883646Scopus ID: 2-s2.0-84906994141OAI: oai:DiVA.org:kth-168111DiVA: diva2:817085
Conference
IEEE International Conference on Communications (ICC) 2014, Sydney, Australia, Jun. 10-14, 2014
Note

QC 20150604. QC 20160314

Available from: 2015-06-04 Created: 2015-05-27 Last updated: 2016-03-14Bibliographically approved

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Oechtering, Tobias

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