Privacy-Preserving Machine Learning Technique Using One-Way Hashing for Enhancing Data Security in the Nigerian Healthcare Ecosystem

Domain:

healthcare

Record type:

paper

Creator:

DamFèsAim

Publisher:

Host:

Safeguarding healthcare data in Nigeria remains a pressing challenge, complicated by
fragmented infrastructure, limited resources, and evolving regulatory frameworks. This paper
presents a conceptual analysis of one-way hashing as a lightweight cryptographic technique for
pseudonymizing patient identifiers within federated learning pipelines. By situating hashing in
contrast to heavier cryptographic methods such as homomorphic encryption and secure
multiparty computation, the study highlights its relative efficiency, scalability, and compliance
with the Nigeria Data Protection Regulation (NDPR, 2019) and the Nigeria Data Protection Act
(NDPA, 2023). Through analytical benchmarking and illustrative scenarios, hashing is shown to
offer a pragmatic balance between privacy preservation and operational feasibility in
resource-constrained healthcare environments. The paper concludes that one-way hashing
provides a viable conceptual pathway for operationalizing privacy-preserving machine learning
in Nigerian healthcare systems, while laying the foundation for future empirical validation.

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