31 citations · 53 across the 18 of their papers we have counts for
6 papers · 1 filter
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.…
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…