Document Type

Journal Article

Journal Article Version

Final Version / Publisher's PDF (peer-reviewed)

Publication Date

2-1-2025

Abstract

Child labor has been on the rise in recent years, which necessitates improved identification and reporting mechanisms. Current labor management systems, which are often manual or article-based, lack traceability, audit, privacy, security, and trust features. This leads to challenges in detecting and reporting violations, particularly in large or remote areas. The persistence of this issue undermines the achievement of Sustainable Development Goals (SDGs) and highlights the important role of Corporate Social Responsibility (CSR) in addressing this challenge. Our article proposes a solution combining machine learning and blockchain to automate child labor detection and ensure traceable, auditable, private, and secure reporting. Utilizing Decentralized Proxy Re-Encryption (DPRE), Zero-Knowledge Proofs (ZKPs), and oracles on the Ethereum blockchain, with decentralized storage, our approach maintains privacy and transparency. We present a child labor detection model using Mask2Former and ResNet-18 Convolutional Neural Network (CNN) to achieve high accuracy and reliability. The model’s performance is evaluated using various metrics, achieving an accuracy rate of 89.45%, a precision score of 0.906, and a recall score of 0.9332. Additionally, we assess smart contracts for cost-efficiency and security, and discuss the solution’s generalizability, challenges, and practical implications. We make the source code of our solution publicly available on GitHub.

Published in

Distributed Ledger Technologies: Research and Practice

Keywords

Blockchain, Child Labor, Object Detection, Social Change, Sustainable Development, Technological Innovation

License for Reuse

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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