Journal of Cyber Security and Risk Auditing

Journal of Cyber Security and Risk Auditing

ISSN: 3079-5354 (Online)

Publishing model:

: Open access
Scopus Indexed
2025
14.7

CiteScore

Q1
open accessOpen Access

Article

👁️14views

A Privacy-Preserving Federated Learning Framework with Fully Homomorphic Encryption for Reproductive Health Analytics

by 

Abass Hassan Orcid link ;

Sheikh Umar Mushtaq Orcid link ;

Hussein Edrees Orcid link ;

Amier Alquatesh Orcid link

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Published: 2026/06/28

Abstract

With the increasing use of cloud analytics technology, machine learning is now being used to help with fertility tracking and predict risks of pregnancy. However, reproductive health data is considered highly sensitive data, and with traditional analytics training, data must be sent to an external server, which is raising giant red flags regarding data privacy and security. This paper will address these issues by proposing a privacy-preserving analytics platform for fertility and pregnancy data by combining Federated Learning (FL) with Fully Homomorphic Encryption (FHE) technology. FL will be utilized so that multiple hospitals can collaborate and come up with a shared model for pregnancy risks. However, with FL, inference leaks occur when data is sent to the server, which compromises sensitive data. To address inference leaks, we will be using the Cheon-Kim-Kim-Song (CKKS) method to encrypt data before it is sent to the server, which will then be aggregated with other data without any sensitive reproductive health data being compromised or exposed during training. We will be using TenSEAL and Scikit-learn to implement our proposed framework and will be testing it with the Maternal Health Risk Dataset. Our results will show that our proposed FL+FHE model is able to achieve reliable prediction accuracy with reasonable encryption overhead.

Keywords

ReproductiveHomomorphic EncryptionMaternalFederated Learningand Cloud.

A Privacy-Preserving Federated Learning Framework with Fully Homomorphic Encryption for Reproductive Health Analytics is licensed under CC BY 4.0CCBY

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