AuthorsA. B. Ovesen, T. S. Nordmo, H. D. Johansen, M. Riegler, P. Halvorsen and D. Johansen
TitleFile System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments
AfilliationMachine Learning
Project(s)Department of Holistic Systems
StatusPublished
Publication TypeJournal Article
Year of Publication2021
JournalInformation
Volume12
Issue10
Pagination430
Date Published10/2021
PublisherMDPI
Abstract


In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of commercial fishery surveillance operations. This is in response to potentially privacy-infringing mandates from some governments to combat overfishing and other sustainability challenges. Our approach is to deploy sensory devices and distributed artificial intelligence algorithms on mobile, offshore fishing vessels and at mainland central control centers. To facilitate this, we need a novel data plane supporting efficient, available, secure, tamper-proof, and compliant data management in this weakly connected offshore environment. We have built our first prototype of Dorvu, a novel distributed file system in this context. Our devised architecture, the design trade-offs among conflicting properties, and our initial experiences are further detailed in this paper.
 

 
URLhttps://www.mdpi.com/2078-2489/12/10/430
DOI10.3390/info12100430
Citation Key28094

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