Enhancing data privacy through a decentralised predictive model with blockchain-based revenue. (14th May 2021)

• Record Type: Journal Article
• Authors: Rahmadika, Sandi; Rhee, Kyung-Hyune
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 37:Number 1(2021)
• Page Start: 1
• Page End: 15
• Publication Date: 2021-05-14
• Subjects: blockchain -- decentralised revenue -- decentralised training -- federated learning -- predictive model -- user privacy; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

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.