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Privacy Preserving Data Mining as Proof of Useful Work: Exploring an AI/Blockchain Design

Privacy Preserving Data Mining as Proof of Useful Work: Exploring an AI/Blockchain Design
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Author(s): Hjalmar K. Turesson (York University, Canada), Henry Kim (blockchain.lab, York University, Canada), Marek Laskowski (blockchain.lab, York University, Canada) and Alexandra Roatis (Aion Network, Canada)
Copyright: 2021
Volume: 32
Issue: 1
Pages: 17
Source title: Journal of Database Management (JDM)
Editor(s)-in-Chief: Keng Siau (City University of Hong Kong, Hong Kong SAR)
DOI: 10.4018/JDM.2021010104

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Abstract

Blockchains rely on a consensus among participants to achieve decentralization and security. However, reaching consensus in an online, digital world where identities are not tied to physical users is a challenging problem. Proof-of-work provides a solution by linking representation to a valuable, physical resource. While this has worked well, it uses a tremendous amount of specialized hardware and energy, with no utility beyond blockchain security. Here, the authors propose an alternative consensus scheme that directs the computational resources to the optimization of machine learning (ML) models – a task with more general utility. This is achieved by a hybrid consensus scheme relying on three parties: data providers, miners, and a committee. The data provider makes data available and provides payment in return for the best model, miners compete about the payment and access to the committee by producing ML optimized models, and the committee controls the ML competition.

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