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  4. An adversarially robust data market for spatial, crowd-sourced data
 
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An adversarially robust data market for spatial, crowd-sourced data
File(s)
3703464.pdf (171.29 MB)
Accepted version
Author(s)
Kharman, Aida Manzano
Jursitzky, Christian
Zhou, Quan
Ferraro, Pietro
Marecek, Jakub
more
Type
Journal Article
Abstract
We describe an architecture for a decentralised data market for applications in which agents are incentivised to collaborate to crowd-source their data. The architecture is designed to reward data that furthers the market’s collective goal, and distributes reward fairly to all those that contribute with their data. We show that the architecture is resilient to Sybil, wormhole and data poisoning attacks. In order to evaluate the resilience of the architecture, we characterise its breakdown points for various adversarial threat models in an automotive use case.
Date Issued
2025-12-01
Date Acceptance
2024-10-20
Citation
Distributed Ledger Technologies: Research and Practice, 2025, 4 (4), pp.1-20
URI
https://hdl.handle.net/10044/1/117185
URL
https://doi.org/10.1145/3703464
DOI
https://www.dx.doi.org/10.1145/3703464
ISSN
2769-6472
Publisher
Association for Computing Machinery (ACM)
Start Page
1
End Page
20
Journal / Book Title
Distributed Ledger Technologies: Research and Practice
Volume
4
Issue
4
Copyright Statement
Copyright © 2025 Copyright Owner. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
http://creativecommons.org/licenses/by/4.0/
Publication Status
Published
Article Number
33
Date Publish Online
2025-10-08
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