Identification of Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel. - Ali Burak Ünal; Öznur Tastan (CoRR, 2016)

A Framework with Randomized Encoding for a Fast Privacy Preserving Calculation of Non-linear Kernels for Machine Learning Applications in Precision Medicine. - Ali Burak Ünal; Mete Akgün; Nico Pfeifer (CANS, 2019)

Privacy-preserving SVM on Outsourced Genomic Data via Secure Multi-party Computation. - Huajie Chen; Ali Burak Ünal; Mete Akgün; Nico Pfeifer (IWSPA@CODASPY, 2020)

Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework. - Efe Bozkir; Ali Burak Ünal; Mete Akgün; Enkelejda Kasneci; Nico Pfeifer (ETRA Short Papers, 2020)

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 (AAAI, 2021)

Identifying disease-causing mutations with privacy protection. - Mete Akgün; Ali Burak Ünal; Bekir Ergüner; Nico Pfeifer; Oliver Kohlbacher (Bioinform., 2021)

PAMOGK: a pathway graph kernel-based multiomics approach for patient clustering. - Yasin Ilkagan Tepeli; Ali Burak Ünal; Furkan Mustafa Akdemir; Öznur Tastan (Bioinform., 2021)

ppAUC: Privacy Preserving Area Under the Curve with Secure 3-Party Computation. - Ali Burak Ünal; Nico Pfeifer; Mete Akgün (CoRR, 2021)

CECILIA: Comprehensive Secure Machine Learning Framework. - Ali Burak Ünal; Mete Akgün; Nico Pfeifer (CoRR, 2022)

Towards a Complete Privacy Preserving Machine Learning Pipeline. - Ali Burak Ünal (2022)

ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and Precision-Recall Curves. - Ali Burak Ünal; Nico Pfeifer; Mete Akgün (NSS, 2023)

A Privacy-Preserving Framework for Collaborative Machine Learning with Kernel Methods. - Anika Hannemann; Ali Burak Ünal; Arjhun Swaminathan; Erik Buchmann; Mete Akgün (TPS-ISA, 2023)

A Privacy-Preserving Federated Learning Approach for Kernel methods. - Anika Hannemann; Ali Burak Ünal; Arjhun Swaminathan; Erik Buchmann; Mete Akgün (CoRR, 2023)

Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach. - Sofiane Ouaari; Ali Burak Ünal; Mete Akgün; Nico Pfeifer (CoRR, 2023)

Private, Efficient and Scalable Kernel Learning for Medical Image Analysis. - Anika Hannemann; Arjhun Swaminathan; Ali Burak Ünal; Mete Akgün (CIBB, 2024)

Privacy Preserving Data Imputation via Multi-Party Computation for Medical Applications. - Julia Jentsch; Ali Burak Ünal; Seyma Selcan Magara; Mete Akgün (HealthCom, 2024)

FHAUC: Privacy Preserving AUC Calculation for Federated Learning using Fully Homomorphic Encryption. - Cem Ata Baykara; Ali Burak Ünal; Mete Akgün (CoRR, 2024)

PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies. - Arjhun Swaminathan; Anika Hannemann; Ali Burak Ünal; Nico Pfeifer; Mete Akgün (CoRR, 2024)

Accelerating Privacy-Preserving Medical Record Linkage: A Three-Party MPC Approach. - Seyma Selcan Magara; Noah Dietrich; Ali Burak Ünal; Mete Akgün (CoRR, 2024)

Privacy Preserving Federated Unsupervised Domain Adaptation with Application to Age Prediction from DNA Methylation Data. - Cem Ata Baykara; Ali Burak Ünal; Nico Pfeifer; Mete Akgün (CoRR, 2024)

A privacy-preserving approach for cloud-based protein fold recognition. - Ali Burak Ünal; Nico Pfeifer; Mete Akgün (Patterns, 2024)

Secure and Efficient Logistic Regression With Secret-Sharing MPC and Differential Privacy. - Seyma Selcan Magara; Ipek Motorcu; Emin Sahin Mektepli; Cem Ata Baykara; Ali Burak Ünal; Mete Akgün (IEEE Access, 2025)

Federated Learning for Epileptic Seizure Prediction Across Heterogeneous EEG Datasets. - Cem Ata Baykara; Saurav Raj Pandey; Ali Burak Ünal; Harlin Lee; Mete Akgün (CoRR, 2025)