Published in ICLR, 2025
This paper introduces LSI a novel measure of data informativeness founded in information theory.
Recommended citation: Kaiser, J., Schwethelm, K., Rueckert, D., & Kaissis, G. Laplace Sample Information: Data Informativeness Through a Bayesian Lens. In The Thirteenth International Conference on Learning Representations. https://arxiv.org/pdf/2505.15303?
Published in TMLR, 2025
This paper explores a threat model, where the attacker has knowledge about the underlying data distribution
Recommended citation: Schwethelm, K., Kaiser, J., Knolle, M., Lockfisch, S., Rueckert, D., & Ziller, A. (2024). Visual Privacy Auditing with Diffusion Models. arXiv preprint arXiv:2403.07588. https://arxiv.org/pdf/2403.07588
Published in SatML, 2025
This paper explores and compares different approaches for active learning in differential privacy
Recommended citation: Schwethelm, K., Kaiser, J., Kuntzer, J., Yiğitsoy, M., Rückert, D., & Kaissis, G. (2025, April). Differentially Private Active Learning: Balancing Effective Data Selection and Privacy. In 2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) (pp. 858-878). IEEE. https://arxiv.org/pdf/2410.00542
Published in Trustworthy AI in Medical Imaging. Academic Press, 2025
This work outlines the application of differential privacy in the medical domain describing opportunities and pitfalls.
Recommended citation: Kaiser, Johannes, Tamara Mueller, and Georgios Kaissis. "Differential privacy in medical imaging applications." Trustworthy AI in Medical Imaging. Academic Press, 2025. 411-424. https://www.sciencedirect.com/science/article/abs/pii/B9780443237614000328