An Intelligent Framework for Secure and Scalable Child Abuse Data Management Using Machine Learning and Blockchain

Abstract

Child abuse remains a critical global concern requiring efficient, secure, and scalable data management systems to support early detection and intervention. However, existing centralized approaches suffer from data fragmentation, limited interoperability, and significant privacy and security challenges. This paper proposes an intelligent hybrid framework that integrates machine learning and blockchain technologies to address these limitations. The framework employs advanced machine learning models for predictive analytics and risk classification, enabling early identification of potential abuse cases from heterogeneous data sources. Simultaneously, blockchain technology is utilized to ensure data integrity, decentralization, and secure access control through immutable ledgers and smart contracts. The system architecture incorporates multi-source data acquisition, preprocessing with anonymization, and off-chain storage mechanisms to enhance scalability while preserving privacy. Experimental results demonstrate that the proposed approach achieves high predictive performance, with the Neural Network model attaining an accuracy of 94.1%, outperforming other baseline models. Furthermore, blockchain integration ensures tamper-proof data management, transparent audit trails, and efficient multi-stakeholder collaboration. The findings highlight the effectiveness of combining machine learning and blockchain in developing secure and intelligent child protection systems. The proposed framework offers a scalable and privacy-preserving solution suitable for real-world deployment across healthcare, social services, and law enforcement domains. Future work will focus on integrating federated learning and optimizing system performance for large-scale applications.