Deploying Trusted and Immutable Predictive Models on a Public Blockchain Network

Volume 9, Issue 3, Page No 72-83, 2024

Author’s Name: Brandon Wetzel, Haiping Xu

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Computer and Information Science Department, University of Massachusetts Dartmouth, Dartmouth, MA 02747, USA

^a)whom correspondence should be addressed. E-mail: hxu@umassd.edu

Adv. Sci. Technol. Eng. Syst. J. 9(3), 72-83 (2024); DOI: 10.25046/aj090307

Keywords: Machine learning, Predictive model, Public blockchain, Smart contract, On-chain storage

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Abstract

Machine learning-based predictive models often face challenges, particularly biases and a lack of trust in their predictions when deployed by individual agents. Establishing a robust deployment methodology that supports validating the accuracy and fairness of these models is a critical endeavor. In this paper, we introduce a novel approach to deploying predictive models, such as pre-trained neural network models, in a public blockchain network using smart contracts. Smart contracts are encoded in our approach as self-executing protocols for storing various parameters of the predictive models. We develop efficient algorithms for uploading and retrieving model parameters from smart contracts on a public blockchain, thereby ensuring the trustworthiness and immutability of the stored models, making them available for testing and validation by all peers within the network. In addition, users can rate and comment on the models, which are permanently recorded in the blockchain. To demonstrate the effectiveness of our approach, we present a case study focusing on storing vehicle price prediction models and review comments. Our experimental results show that deploying predictive models on a public blockchain network provides a proficient and reliable way to ensure model security, immutability, and transparency.

Received: 25 March 2024, Revised: 12 May 2024, Accepted: 29 May 2024, Published Online: 16 June 2024

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Deploying Trusted and Immutable Predictive Models on a Public Blockchain Network