5 citations · 12 across the 6 of their papers we have counts for
6 papers
PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies
Arjhun Swaminathan, Anika Hannemann, Ali Burak Ünal +2
Genome-wide association studies are pivotal in understanding the genetic underpinnings of complex traits and diseases. Collaborative, multi-site GWAS aim to enhance statistical pow…
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…
COmic: Convolutional Kernel Networks for Interpretable End-to-End Learning on (Multi-)Omics Data
Jonas C. Ditz, Bernhard Reuter, Nico Pfeifer
Motivation: The size of available omics datasets is steadily increasing with technological advancement in recent years. While this increase in sample size can be used to improve th…
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…