An adversarially robust data-market for spatial, crowd-sourced data

TL;DR

This paper proposes a decentralized data market architecture that incentivizes collaboration in crowd-sourcing spatial data, ensuring fairness and robustness against various adversarial attacks, with evaluated resilience in automotive scenarios.

Contribution

It introduces a novel architecture for a resilient, fair, and incentivized decentralized data market for spatial data collection.

Findings

Resilient to Sybil, wormhole, and data poisoning attacks.

Characterized breakdown points under different threat models.

Validated in an automotive use case.

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.