5 citations · 12 across the 6 of their papers we have counts for
4 papers · 1 filter
Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach
Sofiane Ouaari, Ali Burak Ünal, Mete Akgün +1
Several domains increasingly rely on machine learning in their applications. The resulting heavy dependence on data has led to the emergence of various laws and regulations around…
Bringing the Algorithms to the Data -- Secure Distributed Medical Analytics using the Personal Health Train (PHT-meDIC)
Marius de Arruda Botelho Herr, Michael Graf, Peter Placzek +11
The need for data privacy and security -- enforced through increasingly strict data protection regulations -- renders the use of healthcare data for machine learning difficult. In…
CECILIA: Comprehensive Secure Machine Learning Framework
Ali Burak Ünal, Nico Pfeifer, Mete Akgün
Since ML algorithms have proven their success in many different applications, there is also a big interest in privacy preserving (PP) ML methods for building models on sensitive da…
ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and Precision-Recall Curves
Ali Burak Ünal, Nico Pfeifer, Mete Akgün
Computing an AUC as a performance measure to compare the quality of different machine learning models is one of the final steps of many research projects. Many of these methods are…