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    Advancing privacy and security in machine learning through homomorphic encryption and explainable AI
    (Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2026-03-05) Abou Harb, Mhd Raja; Çeliktaş, Barış; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Doktora Programı; Işık University, School of Graduate Studies, Ph.D. in Computer Engineering
    The importance of data privacy in cloud-based Machine Learning is paramount, particularly in sectors such as healthcare and finance. Balancing robust privacy protection with high model accuracy remains a significant challenge. In this study, we propose a privacy-preserving framework utilizing ANNs on homomorphically encrypted data. To mitigate the computational complexity of non-linear activation functions (Sigmoid and Tanh), we developed lightweight, ANN-based estimators specifically designed for encrypted environments. Our experimental results demonstrate that these estimators significantly outperform traditional polynomial and piecewise linear methods, reducing MSE by up to 96% while improving accuracy and F1-scores. Our method achieved 97.70% accuracy and 0.9997 AUC on the MNIST dataset, validating its effectiveness. In real-world applications, we applied the approach to dyslexia detection using QEEG data, observing only minor performance degradation (2.66% accuracy, 3.86% AUC) compared to plaintext inference. Furthermore, a case study on the UCI Heart Disease dataset yielded 85.25% accuracy in encrypted inference, matching plaintext performance. Finally, we integrated the SHAP algorithm to ensure transparency for encrypted outputs. Our findings confirm that this approach successfully balances privacy, performance, and explainability, making it highly suitable for sensitive ML applications.