Anika Hannemann

According to our database, Anika Hannemann authored at least 9 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2026

Understanding the Resource Cost of Fully Homomorphic Encryption in Quantum Federated Learning.

Lukas Böhm, Arjhun Swaminathan, Anika Hannemann, Erik Buchmann

CoRR, March, 2026

2025

Privacy-preserving distributed learning: balancing trade-offs in precision medicine.

Anika Hannemann

PhD thesis, 2025

2024

PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies.

Arjhun Swaminathan, Anika Hannemann, Ali Burak Ünal, Nico Pfeifer, Mete Akgün

CoRR, 2024

Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs.

Anika Hannemann, Jan Ewald, Leo Seeger, Erik Buchmann

Proceedings of the Computational Science - ICCS 2024, 2024

Differentially Private Multi-Label Learning Is Harder Than You'd Think.

Anika Hannemann, Benjamin Friedl, Erik Buchmann

Proceedings of the IEEE European Symposium on Security and Privacy Workshops, 2024

Private, Efficient and Scalable Kernel Learning for Medical Image Analysis.

Anika Hannemann, Arjhun Swaminathan, Ali Burak Ünal, Mete Akgün

Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2024

2023

A Privacy-Preserving Federated Learning Approach for Kernel methods.

Anika Hannemann, Ali Burak Ünal, Arjhun Swaminathan, Erik Buchmann, Mete Akgün

CoRR, 2023

A Privacy-Preserving Framework for Collaborative Machine Learning with Kernel Methods.

Anika Hannemann, Ali Burak Ünal, Arjhun Swaminathan, Erik Buchmann, Mete Akgün

Proceedings of the 5th IEEE International Conference on Trust, 2023

Is Homomorphic Encryption Feasible for Smart Mobility?

Anika Hannemann, Erik Buchmann

Proceedings of the 18th Conference on Computer Science and Intelligence Systems, 2023