31 citations · 46 across the 16 of their papers we have counts for
17 papers
FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains
Jolle Verhoog, Ali Burak Ünal, Holger Caesar
Robust perception in intelligent vehicles demands 3D object detectors that remain dependable under domain shifts, such as changes in time of day, location, or weather. However, due…
Privacy-Preserving Detection of Rare Disease-Associated Cell Subsets via Secure Multi-Party Computation
Ş. Selcan Magara, Esther Havemann, Debora Jutz +2
The detection of rare disease-associated cell subsets from high-dimensional single-cell measurements is critical for understanding diseases such as leukaemia and viral infections.…
Scaling Unsupervised Multi-Source Federated Domain Adaptation through Group-Wise Discrepancy Minimization
Larissa Reichart, Cem Ata Baykara, Ali Burak Ünal +2
Unsupervised multi-source domain adaptation (UMDA) leverages labeled data from multiple source domains to generalize to an unlabeled target. While federated UMDA addresses privacy…
Accurate and Private Diagnosis of Rare Genetic Syndromes from Facial Images with Federated Deep Learning
Ali Burak Ünal, Cem Ata Baykara, Peter Krawitz +1
Machine learning has shown promise in facial dysmorphology, where characteristic facial features provide diagnostic clues for rare genetic disorders. GestaltMatcher, a leading fram…
Federated Learning for Epileptic Seizure Prediction Across Heterogeneous EEG Datasets
Cem Ata Baykara, Saurav Raj Pandey, Ali Burak Ünal +2
Developing accurate and generalizable epileptic seizure prediction models from electroencephalography (EEG) data across multiple clinical sites is hindered by patient privacy regul…
Accelerating Privacy-Preserving Medical Record Linkage: A Three-Party MPC Approach
Şeyma Selcan Mağara, Noah Dietrich, Ali Burak Ünal +1
Record linkage is a crucial concept for integrating data from multiple sources, particularly when datasets lack exact identifiers, and it has diverse applications in real-world dat…
Private, Efficient and Scalable Kernel Learning for Medical Image Analysis
Anika Hannemann, Arjhun Swaminathan, Ali Burak Ünal +1
Medical imaging is key in modern medicine. From magnetic resonance imaging (MRI) to microscopic imaging for blood cell detection, diagnostic medical imaging reveals vital insights…
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…
Privacy Preserving Data Imputation via Multi-party Computation for Medical Applications
Julia Jentsch, Ali Burak Ünal, Şeyma Selcan Mağara +1
Handling missing data is crucial in machine learning, but many datasets contain gaps due to errors or non-response. Unlike traditional methods such as listwise deletion, which are…
Enabling Privacy-preserving Model Evaluation in Federated Learning via Fully Homomorphic Encryption
Cem Ata Baykara, Ali Burak Ünal, Mete Akgün
Federated learning has become increasingly widespread due to its ability to train models collaboratively without centralizing sensitive data. While most research on FL emphasizes p…
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…
A Privacy-Preserving Federated Learning Approach for Kernel methods
Anika Hannemann, Ali Burak Ünal, Arjhun Swaminathan +2
It is challenging to implement Kernel methods, if the data sources are distributed and cannot be joined at a trusted third party for privacy reasons. It is even more challenging, i…
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…
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare
Ali Burak Ünal, Mete Akgün, Nico Pfeifer
To train sophisticated machine learning models one usually needs many training samples. Especially in healthcare settings these samples can be very expensive, meaning that one inst…
Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework
Efe Bozkir, Ali Burak Ünal, Mete Akgün +2
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 bl…
Identification of Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel
Ali Burak Ünal, Öznur Taştan
Characterizing patient somatic mutations through next-generation sequencing technologies opens up possibilities for refining cancer subtypes. However, catalogues of mutations revea…