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

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Child abuse remains a critical global concern requiring efficient, secure, and scalable datamanagement systems to support early detection and intervention. However, existing centralizedapproaches suffer from data fragmentation, limited interoperability, and significant privacy andsecurity challenges. This paper proposes an intelligent hybrid framework that integrates machinelearning and blockchain technologies to address these limitations. The framework employsadvanced machine learning models for predictive analytics and risk classification, enabling earlyidentification of potential abuse cases from heterogeneous data sources. Simultaneously,blockchain technology is utilized to ensure data integrity, decentralization, and secure accesscontrol through immutable ledgers and smart contracts.The system architecture incorporates multi-source data acquisition, preprocessing withanonymization, and off-chain storage mechanisms to enhance scalability while preserving privacy.Experimental results demonstrate that the proposed approach achieves high predictiveperformance, with the Neural Network model attaining an accuracy of 94.1%, outperforming otherbaseline 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 indeveloping secure and intelligent child protection systems. The proposed framework offers ascalable and privacy-preserving solution suitable for real-world deployment across healthcare,social services, and law enforcement domains. Future work will focus on integrating federatedlearning and optimizing system performance for large-scale applications.

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