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

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Title: A Privacy-Preserving Federated Learning Framework with Fully Homomorphic Encryption for Reproductive Health Analytics.
Authors: Hassan, Abass1, mohdabass0202@gmail.com, Mushtaq, Sheikh Umar2, shiekhumar12@gmail.com, Edrees, Hussein3, Alquatesh, Amier3, aalqatish@kfu.edu.sa
Source: Journal of Cyber Security & Risk Auditing; 2026, Vol. 2026 Issue 2, p265-280, 16p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 195660654
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Cyber+Security+%26+Risk+Auditing%22">Journal of Cyber Security & Risk Auditing</searchLink>; 2026, Vol. 2026 Issue 2, p265-280, 16p
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.63180/jcsra.thestap.2026.2.7
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      – Code: eng
        Text: English
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        PageCount: 16
        StartPage: 265
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      – TitleFull: A Privacy-Preserving Federated Learning Framework with Fully Homomorphic Encryption for Reproductive Health Analytics.
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            NameFull: Hassan, Abass
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            NameFull: Mushtaq, Sheikh Umar
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            NameFull: Edrees, Hussein
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            NameFull: Alquatesh, Amier
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              Text: 2026
              Type: published
              Y: 2026
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              Value: 2026
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              Value: 2
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            – TitleFull: Journal of Cyber Security & Risk Auditing
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