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Mete Akgün

Private, Efficient and Scalable Kernel Learning for Medical Image Analysis

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Oct 21, 2024
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Distributed and Secure Kernel-Based Quantum Machine Learning

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Aug 16, 2024
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Privacy Preserving Data Imputation via Multi-party Computation for Medical Applications

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May 29, 2024
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Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach

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Sep 08, 2023
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A Privacy-Preserving Federated Learning Approach for Kernel methods

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Jun 05, 2023
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Bringing the Algorithms to the Data -- Secure Distributed Medical Analytics using the Personal Health Train

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Dec 07, 2022
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CECILIA: Comprehensive Secure Machine Learning Framework

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Feb 07, 2022
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ppAUC: Privacy Preserving Area Under the Curve with Secure 3-Party Computation

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Feb 17, 2021
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ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare

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Dec 04, 2020
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Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework

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Nov 06, 2019
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