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

Domaine:

healthcare

Type de record:

paper
Créateur:
DamFèsAim
Éditeur:
Zenodo
Hôte:avatar

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.

Visit

doi.org

Tags

Healthcare data protection; Federated learning; One-way hashing; Pseudonymizations; Privacy-preserving machine learning; Conceptual analysis; Nigeria Data Protection Regulation (NDPR); Nigeria Data Protection Act (NDPA); Cryptographic complexity; Resource-constrained environments

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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