Welcome to the InfoSci Platform
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

Hjalmar K. Turesson, Henry Kim, Marek Laskowski, Alexandra Roatis
Copyright: © 2023 |Pages: 19
ISBN13: 9781668471326|ISBN10: 1668471329|EISBN13: 9781668471333
DOI: 10.4018/978-1-6684-7132-6.ch024
Cite Chapter Cite Chapter

MLA

Turesson, Hjalmar K., et al. "Privacy Preserving Data Mining as Proof of Useful Work: Exploring an AI/Blockchain Design." Research Anthology on Convergence of Blockchain, Internet of Things, and Security, edited by Information Resources Management Association, IGI Global, 2023, pp. 402-420. https://doi.org/10.4018/978-1-6684-7132-6.ch024

APA

Turesson, H. K., Kim, H., Laskowski, M., & Roatis, A. (2023). Privacy Preserving Data Mining as Proof of Useful Work: Exploring an AI/Blockchain Design. In I. Management Association (Ed.), Research Anthology on Convergence of Blockchain, Internet of Things, and Security (pp. 402-420). IGI Global. https://doi.org/10.4018/978-1-6684-7132-6.ch024

Chicago

Turesson, Hjalmar K., et al. "Privacy Preserving Data Mining as Proof of Useful Work: Exploring an AI/Blockchain Design." In Research Anthology on Convergence of Blockchain, Internet of Things, and Security, edited by Information Resources Management Association, 402-420. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-7132-6.ch024

Export Reference

Mendeley
Favorite

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.