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 ist Tenure-Track Faculty am CISPA Helmholtz-Zentrum für Informationssicherheit. Am CISPA ist sie Co-Leiterin des SprintML Lab (Secure, Private, Robust, Interpretable, and Trustworthy Machine Learning), in dem sie zur Weiterentwicklung von vertrauenswürdigem Machine Learning forscht. Zuvor war sie Postdoctoral Fellow am Vector Institute for Artificial Intelligence, betreut von Prof. Dr. Nicolas Papernot. Vor ihrem Wechsel an das Vector Institute war sie Doktorandin an der Freien Universität Berlin sowie wissenschaftliche Mitarbeiterin am Fraunhofer-Institut für Angewandte und Integrierte Sicherheit (AISEC).
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) CDI: Copyrighted Data Identification in Diffusion Models
International Conference on Machine Learning (ICML) Unlocking Post-hoc Dataset Inference with Synthetic Data
International Conference on Machine Learning (ICML) Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
International Conference on Machine Learning (ICML) Privacy Attacks on Image AutoRegressive Models
International Conference on Learning Representations (ICLR) Precise Parameter Localization for Textual Generation in Diffusion Models
National Conference of the American Association for Artificial Intelligence (AAAI) Differentially Private Prototypes for Imbalanced Transfer Learning
International Conference on Learning Representations (ICLR) Captured by Captions: On Memorization and its Mitigation in CLIP Models
International Conference on Learning Representations (ICLR) Differentially Private Federated Learning with Time-Adaptive Privacy Spending
European Conference on Artificial Intelligence (ECAI) Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
NeurIPS-Workshop (NeurIPS-W) Auditing Empirical Privacy Protection for Adaptations of Large Language Models