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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

Privacy-preserving machine learning techniques ensuring data confidentiality and model accuracy in federated learning environments

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pp. 1887–1895Vol. 47Issue 5-BMay 2026DOI: 10.47974/JIOS-2281XML
Received:
01 Apr 2025
Published Online:
23 Apr 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2281
Pages:
1887–1895

Abstract

Federated learning (FL) lets multiple people work together to train a model without storing private data in one place. This has a lot of promise for making AI more privacy-aware. However, FL is still open to reasoning attacks, data leaks, and loss of accuracy. This research looks into different types of privacy-preserving methods that balance model performance with privacy. It focuses on differential privacy, safe multiparty computing, and homomorphic encryption. The paper starts with a mathematical framework and then suggests a way to do distribute training that includes noise input and safe aggregation. The results of the experiments show that there are trade-offs between accuracy, extra contact, and privacy leaks. Findings show that carefully regulated methods keep information private while keeping competitive model usefulness in settings with many computers.

Keywords

Subject Classifications

68P2768T01

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