File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments

• Published in: Information
• Main Authors: Aril Bernhard Ovesen, Tor-Arne Schmidt Nordmo, Håvard Dagenborg Johansen, Michael Alexander Riegler, Pål Halvorsen, Dag Johansen
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: edge computing; privacy preservation; artificial intelligence; file systems; machine learning; digital forensics; Information technology; T58.5-58.64; Arctic; Arctic Ocean
• Online Access: https://doi.org/10.3390/info12100430; https://doaj.org/article/742bea4cd9ac4ae1aac27d93c57ea4c3

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.