Differential privacy in medical imaging applications
Kaiser, Johannes, Tamara Mueller, and Georgios Kaissis. "Differential privacy in medical imaging applications." Trustworthy AI in Medical Imaging. Academic Press, 2025. 411-424.
CV
Education
- B.S. in GitHub, GitHub University, 2012
- M.S. in Jekyll, GitHub University, 2014
- Ph.D in Version Control Theory, GitHub University, 2018 (expected)
Work experience
- Summer 2015: Research Assistant
- Github University
- Duties included: Tagging issues
- Supervisor: Professor Git
- Fall 2015: Research Assistant
- Github University
- Duties included: Merging pull requests
- Supervisor: Professor Hub
Skills
- Skill 1
- Skill 2
- Sub-skill 2.1
- Sub-skill 2.2
- Sub-skill 2.3
- Skill 3
Publications
Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
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.
Visual Privacy Auditing with Diffusion Models
Schwethelm, K., Kaiser, J., Knolle, M., Lockfisch, S., Rueckert, D., & Ziller, A. (2024). Visual Privacy Auditing with Diffusion Models. arXiv preprint arXiv:2403.07588.
Laplace Sample Information: Data Informativeness Through a Bayesian Lens
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.
Talks
Talk on ‘Privacy-Fairness Tradeoff’ at Google Deepmind
Talk at Remote @ Google Deepmind, London, UK
Teaching
Service and leadership
- Currently signed in to 43 different slack teams