Blockchain-Assisted Assurance of Data Integrity in AI Model Training: A Hybrid Optimization Approach for Secure Learning Pipelines - Zakariae, Saidi; Ouidad, Akhrif; El Idrissi Younes, El Bouzekri (Informatica, 2025)
Ensuring the integrity of training data is critical for the development of trustworthy and secure artificial intelligence (AI) systems, particularly in the face of emerging threats such as data poisoning and model inversion attacks. This study proposes a novel hybrid framework that combines blockchain technology with metaheuristic optimization techniques to enhance the robustness of AI model training. The framework leverages blockchain’s immutable ledger to securely record data deltas, thereby guaranteeing provenance, input validity, and traceability throughout the training process. Empirical evaluations on standard benchmark datasets, including simulations of synthetic adversarial attacks, demonstrate that the proposed approach significantly improves model accuracy, transparency, and resilience against integrity breaches. While the results are promising, further research is needed to address scalability challenges in large-scale, real-world AI systems and to evaluate defense performance against a broader spectrum of adversarial techniques. The framework provides practical insights for cybersecurity-conscious AI development, offering a pathway toward the creation of more secure, explainable, and reliable AI applications. This work represents a unique contribution by integrating blockchain with optimization-based AI training, aligning with the increasing demand for robust AI systems in cybersecurity-sensitive environments.