In this section
@article{Kharman:2024:10.1145/3703464,
author = {Kharman, AM and Jursitzky, C and Zhou, Q and Ferraro, P and Marecek, J and Pinson, P and Shorten, R},
doi = {10.1145/3703464},
journal = {Distributed Ledger Technologies: Research and Practice},
title = {An adversarially robust data-market for spatial, crowd-sourced data},
url = {http://dx.doi.org/10.1145/3703464},
year = {2024}
}
TY - JOUR
AB - <jats:p> 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 <jats:italic>Sybil</jats:italic> , <jats:italic>wormhole</jats:italic> , and <jats:italic>data poisoning</jats:italic> 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. </jats:p>
AU - Kharman,AM
AU - Jursitzky,C
AU - Zhou,Q
AU - Ferraro,P
AU - Marecek,J
AU - Pinson,P
AU - Shorten,R
DO - 10.1145/3703464
PY - 2024///
TI - An adversarially robust data-market for spatial, crowd-sourced data
T2 - Distributed Ledger Technologies: Research and Practice
UR - http://dx.doi.org/10.1145/3703464
UR - https://doi.org/10.1145/3703464
ER -
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