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).
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) Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models
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) 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)