Search Results for author: Mete Akgün

Found 3 papers, 0 papers with code

ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and Precision-Recall Curves with Secure 3-Party Computation

no code implementations17 Feb 2021 Ali Burak Ünal, Nico Pfeifer, Mete Akgün

Many of these methods are trained on privacy-sensitive data and there are several different approaches like $\epsilon$-differential privacy, federated machine learning and methods based on cryptographic approaches if the datasets cannot be shared or evaluated jointly at one place.

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

no code implementations4 Dec 2020 Ali Burak Ünal, Mete Akgün, Nico Pfeifer

We address this problem by introducing ESCAPED, which stands for Efficient SeCure And PrivatE Dot product framework, enabling the computation of the dot product of vectors from multiple sources on a third-party, which later trains kernel-based machine learning algorithms, while neither sacrificing privacy nor adding noise.

Dimensionality Reduction

Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework

no code implementations6 Nov 2019 Efe Bozkir, Ali Burak Ünal, Mete Akgün, Enkelejda Kasneci, Nico Pfeifer

Eye tracking is handled as one of the key technologies for applications that assess and evaluate human attention, behavior, and biometrics, especially using gaze, pupillary, and blink behaviors.

Eye Tracking Gaze Estimation

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