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Yazar: Harb, Mhd Raja Abou × search.filters.applied.f.publicationcategory: Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı ×

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  • Küçük Resim
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    Efficient estimation of Sigmoid and Tanh activation functions for homomorphically encrypted data using Artificial Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2024) Harb, Mhd Raja Abou; Çeliktaş, Barış
    This paper presents a novel approach to estimating Sigmoid and Tanh activation functions using Artificial Neural Networks (ANN) optimized for homomorphic encryption. The proposed method is compared against second-degree polynomial and Piecewise Linear approximations, demonstrating a minor loss in accuracy while maintaining computational efficiency. Our results suggest that the ANN-based estimator is a viable alternative for secure machine learning models requiring privacypreserving computation.
  • Küçük Resim
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    Secure and interpretable dyslexia detection using homomorphic encryption and SHAP-based explanations
    (Institute of Electrical and Electronics Engineers Inc., 2025-10-25) Harb, Mhd Raja Abou; Çeliktaş, Barış; Eroğlu, Günet
    Protecting sensitive healthcare data during machine learning inference is critical, particularly in cloud-based environments. This study addresses the privacy and interpretability challenges in dyslexia detection using Quantitative EEG (QEEG) data. We propose a privacy-preserving framework utilizing Homomorphic Encryption (HE) to securely perform inference with an Artificial Neural Network (ANN). Due to the incompatibility of non-linear activation functions with encrypted arithmetic, we employ a dedicated approximation strategy. To ensure model interpretability without compromising privacy, SHapley Additive exPlanations (SHAP) are computed homomorphically and decrypted client-side. Experimental evaluations demonstrate that the encrypted inference achieves an accuracy of 90.03% and an AUC of 0.8218, reflecting only minor performance degradation compared to plaintext inference. SHAP value comparisons (Spearman correlation = 0.59) validate the reliability of the encrypted explanations. These results confirm that integrating privacy-preserving and explainable AI approaches is feasible for secure, ethical, and compliant healthcare deployments.