Privacy-Preserving AI in Public Health: A Review of Federated Learning, Neural Networks, and Secure Computational Methods for Population Health Data
DOI:
https://doi.org/10.70445/gjus.3.1.2026.77-102Keywords:
Privacy Preserving AI, Federated learning, Neural networks, Differential privacy, Homomorphic encryption, Public health, Data privacy, Population healthAbstract
Artificial intelligence (AI) is revolutionizing public health research by facilitating the secure analysis of sensitive population health data.AI is revolutionizing public health research by allowing for the secure analysis of sensitive population health data. This review covers federated learning, neural networks, and secure computation techniques such as differential privacy, homomorphic encryption, and secure multi-party computation. They are discussed in the context of their use in disease surveillance, disease risk prediction, and disease epidemiology and resource allocation in healthcare. While offering promises of benefits, the key challenges are complexity, heterogeneity of data, privacy attacks, algorithmic biases, and regulatory restrictions. Combining complementary methods for privacy may enhance the security of the data while preserving the performance of the analysis. Going forward, scalable frameworks, explain ability, fairness considerations, and real-world validation are key factors to prioritize for trustworthy public health use cases.
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