Franziska Boenisch
Professor
Researches privacy and security of large AI models, including auditing, red teaming and protection mechanisms with provable guarantees.
Franziska Boenisch's lab at TUM works on trustworthy & safe ML. 39 papers since 2023, 30 at top venues, mostly NeurIPS and ICLR. 14 PhD students.
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 77%
- PhD students
- 14
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 10 papers2026 · T1 9 · collecting
- 15 papers2025 · T1 11
- 7 papers2024 · T1 5
- 7 papers2023 · T1 5
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, AAAI, ACL
Research focuspaper titles that use each topic's words
- auditing2 papers
- Trustworthy & safe ML0 papers
- provable guarantees0 papers
- provable0 papers
- teaming0 papers
Paper list
From the lab homepage.
202610 papers
- The Fortieth AAAI Conference on Artificial Intelligence AI Alignment TrackBeautiful Images, Toxic Words: Understanding and Addressing Offensive Text in Generated Images
- ICLR (T1)Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models
- ICML (T1)Concept Removal for Frontier Image Generative Models
- ICLR (T1)Curation Leaks: Membership Inference Attacks against Data Curation for Machine Learning
- ICLR (T1)Data Provenance for Image Auto-Regressive Generation
- AAAI (T1)Demystifying Foreground-Background Memorization in Diffusion Models
- ICML (T1)Finding DoRI: Discovery of Retained Images in Diffusion Models
- ICLR (T1)Natural Identifiers for Privacy and Data Audits in Large Language Models
- AAAI (T1)On Stealing Graph Neural Network Models
- ICLR (T1)SERUM: Simple, Efficient, Robust, and Unifying Marking for Diffusion-based Image Generation
202515 papers
- ICLR WorkshopAre Watermarks For Diffusion Models Radioactive?
- NeurIPS (T1)BitMark for Infinity: Watermarking Bitwise Autoregressive Image Generative Models
- ICLR (T1)Captured by Captions: On Memorization and its Mitigation in CLIP Models
- CVPR (T1)CDI: Copyrighted Data Identification in Diffusion Models
- ICLR (T1)Differentially Private Federated Learning with Time-Adaptive Privacy Spending
- AAAI (T1)Differentially Private Prototypes for Private Transfer Learning
- ICML (T1)Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
- NeurIPS (T1)Memorization in Graph Neural Networks
- —MUC: Machine Unlearning for Contrastive Learning with Black-box Evaluation
- ICLR (T1)Precise Parameter Localization for Textual Generation in Diffusion Models
- ICML (T1)Privacy Attacks on Image AutoRegressive Models
- ICLR WorkshopPrivacy Auditing for Large Language Models with Natural Identifiers
20247 papers
- ICML Next Generation of AI Safety WorkshopAlignment Calibration: Machine Unlearning for Contrastive Learning under Auditing
- eBioMedicineDecentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data
- NeurIPS (T1)Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models
- NeurIPS (T1)LLM Dataset Inference: Did you train on my dataset?
- NeurIPS (T1)Localizing Memorization in SSL Vision Encoders
- ICLR (T1)Memorization in Self-Supervised Learning Improves Downstream Generalization
- NeurIPS (T1)Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
20237 papers
- NeurIPS (T1)Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders
- NeurIPS (T1)Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models
- NeurIPS (T1)Have it your way: Individualized Privacy Assignment for DP-SGD
- PETSIndividualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees
- ACL (T1)On the privacy risk of in-context learning
- PETSPrivate Multi-Winner Voting for Machine Learning
- NeurIPS (T1)Robust and Actively Secure Serverless Collaborative Learning
Where alumni went
Alumni are not listed on the lab homepage.
Apply to TUMRolling
Not announced yet
Computer Science Doctorate (CIT)
- Round
- Rolling (position-based)
- Opens
- —
- GRE
- See program page
- English
- Very good written and spoken English; no minimum test score
- Fee
- See program page
Confirmed on the program page.
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