Title: Enhancing data privacy through a decentralised predictive model with blockchain-based revenue

Authors: Sandi Rahmadika; Kyung-Hyune Rhee

Addresses: Department of Information Security, Graduate School, Pukyong National University, A12-1305, Daeyeon Campus, Yongso-ro 45, Nam-gu, Busan (48513), South Korea ' Department of IT Convergence and Application Engineering, Pukyong National University, A12-1305, Daeyeon Campus, Yongso-ro 45, Nam-gu, Busan (48513), South Korea

Abstract: Federated learning (FL) permits a vast number of connected to construct deep learning models while keeping their private training data on the device. Rather than uploading the training data and model to the server, FL only sends the local gradients gradually. Hence, FL preserves data privacy by design. FL leverages a decentralised approach where the training data is no longer concentrated. Similarly, blockchain uses the same approach by providing a digital ledger that can cover the flaws in the centralised system. Motivated by the merits of a decentralised approach, we construct a collaborative model of simultaneous distributed learning by employing multiple computing devices over shared memory with blockchain smart contracts as a secure incentive mechanism. The collaborative model preserves a value-driven of distributed learning in enhancing users' privacy. It is supported by blockchain with a secure decentralised incentive technique without having a single point of failure. Furthermore, potential vulnerabilities and plausible defences are also outlined. The experimental results positively recommend that the collaborative model satisfies the design goals.

Keywords: blockchain; decentralised revenue; decentralised training; federated learning; predictive model; user privacy.

DOI: 10.1504/IJAHUC.2021.115104

International Journal of Ad Hoc and Ubiquitous Computing, 2021 Vol.37 No.1, pp.1 - 15

Received: 03 Jun 2020
Accepted: 11 Sep 2020
Published online: 18 May 2021 *

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