Faculty
2025: Werner-von-Siemens-Fellow
2025: ERC Starting Grant
2024: GI Junior-Fellow
2024: Busy Beaver Award "Differential Privacy: Mathematical Foundations and Applications in Machine Learning“, Saarland University
Franziska Boenisch is a tenure-track faculty member at the CISPA Helmholtz Center for Information Security. At CISPA, she co-leads the SprintML Lab (Secure, Private, Robust, Interpretable, and Trustworthy Machine Learning), where her research focuses on advancing trustworthy machine learning. Prior to joining CISPA, she was a Postdoctoral Fellow at the Vector Institute for Artificial Intelligence, working under the supervision of Prof. Dr. Nicolas Papernot. She received her PhD from Freie Universität Berlin, where she also served as a research associate at the Fraunhofer Institute for Applied and Integrated Security (AISEC).
ICML Workshop on Foundation Models in the WIld (ICML-W) POST: A Framework for Privacy of Soft-prompt Transfer
IEEE International Symposium on Information Theory (ISIT) Controlled privacy leakage propagation throughout differential private overlapping grouped learning
IEEE Journal on Selected Areas in Information Theory Controlled privacy leakage propagation throughout overlapping grouped learning
International Conference on Learning Representations (ICLR) Memorization in Self-Supervised Learning Improves Downstream Generalization
Conference on Neural Information Processing Systems (NeurIPS) Localizing Memorization in SSL Vision Encoders
Conference on Neural Information Processing Systems (NeurIPS) Open LLMs are Necessary for Private Adaptations and Outperform their Closed Alternatives
Conference on Neural Information Processing Systems (NeurIPS) Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models
Conference on Neural Information Processing Systems (NeurIPS) Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders
Conference on Neural Information Processing Systems (NeurIPS) Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models
NeurIPS-Workshop (NeurIPS-W)