Is Homomorphic Encryption Feasible for Smart Mobility? - Anika Hannemann; Erik Buchmann (FedCSIS, 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)

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

Differentially Private Multi-Label Learning Is Harder Than You'd Think. - Anika Hannemann; Benjamin Friedl; Erik Buchmann (EuroS&P Workshops, 2024)

Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs. - Anika Hannemann; Jan Ewald; Leo Seeger; Erik Buchmann (ICCS (4), 2024)

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