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)

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

Dynamic k-Anonymity for Electronic Health Records: A Topological Framework. - Arjhun Swaminathan; Mete Akgün (ESORICS Workshops (1), 2024)

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

Distributed and Secure Kernel-Based Quantum Machine Learning. - Arjhun Swaminathan; Mete Akgün (Trans. Mach. Learn. Res., 2025)

Accelerating Targeted Hard-Label Adversarial Attacks in Low-Query Black-Box Settings. - Arjhun Swaminathan; Mete Akgün (CoRR, 2025)