Tracking all members of a honey bee colony over their lifetime. - Franziska Boenisch; Benjamin Rosemann; Benjamin Wild; Fernando Wario; David M. Dormagen; Tim Landgraf (CoRR, 2018)

Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence. - Franziska Boenisch; Benjamin Rosemann; Benjamin Wild; David M. Dormagen; Fernando Wario; Tim Landgraf (Frontiers Robotics AI, 2018)

Applying Differential Privacy to Machine Learning: Challenges and Potentials. - Franziska Boenisch (Krypto-Tag, 2019)

A Survey on Model Watermarking Neural Networks. - Franziska Boenisch (CoRR, 2020)

Side-Channel Attacks on Query-Based Data Anonymization. - Franziska Boenisch; Reinhard Munz; Marcel Tiepelt; Simon Hanisch; Christiane Kuhn; Paul Francis (CCS, 2021)

"I Never Thought About Securing My Machine Learning Systems": A Study of Security and Privacy Awareness of Machine Learning Practitioners. - Franziska Boenisch; Verena Battis; Nicolas Buchmann; Maija Poikela (MuC, 2021)

Privacy Needs Reflection: Conceptional Design Rationales for Privacy-Preserving Explanation User Interfaces. - Peter Sörries; Claudia Müller-Birn; Katrin Glinka; Franziska Boenisch; Marian Margraf; Sabine Sayegh-Jodehl; Matthias Rose (MuC (Workshopband), 2021)

Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning. - Franziska Boenisch; Philip Sperl; Konstantin Böttinger (CoRR, 2021)

Privatsphäre und Maschinelles Lernen. - Franziska Boenisch (Datenschutz und Datensicherheit, 2021)

A Systematic Review on Model Watermarking for Neural Networks. - Franziska Boenisch (Frontiers Big Data, 2021)

The Influence of Training Parameters on Neural Networks' Vulnerability to Membership Inference Attacks. - Oussama Bouanani; Franziska Boenisch (GI-Jahrestagung, 2022)

Dataset Inference for Self-Supervised Models. - Adam Dziedzic; Haonan Duan 002; Muhammad Ahmad Kaleem; Nikita Dhawan; Jonas Guan; Yannis Cattan; Franziska Boenisch; Nicolas Papernot (NeurIPS, 2022)

Personalized PATE: Differential Privacy for Machine Learning with Individual Privacy Guarantees. - Christopher Mühl; Franziska Boenisch (CoRR, 2022)

Bounding Membership Inference. - Anvith Thudi; Ilia Shumailov; Franziska Boenisch; Nicolas Papernot (CoRR, 2022)

Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks. - Tabea Kossen; Manuel A. Hirzel; Vince I. Madai; Franziska Boenisch; Anja Hennemuth; Kristian Hildebrand; Sebastian Pokutta; Kartikey Sharma; Adam Hilbert; Jan Sobesky; Ivana Galinovic; Ahmed A. Khalil; et al. (Frontiers Artif. Intell., 2022)

Secure and Private Machine Learning. - Franziska Boenisch (2022)

When the Curious Abandon Honesty: Federated Learning Is Not Private. - Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot (EuroS&P, 2023)

Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation. - Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot (EuroS&P, 2023)

Have it your way: Individualized Privacy Assignment for DP-SGD. - Franziska Boenisch; Christopher Mühl; Adam Dziedzic; Roy Rinberg; Nicolas Papernot (NeurIPS, 2023)

Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. - Haonan Duan 002; Adam Dziedzic; Nicolas Papernot; Franziska Boenisch (NeurIPS, 2023)

Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. - Jan Dubinski; Stanislaw Pawlak; Franziska Boenisch; Tomasz Trzcinski; Adam Dziedzic (NeurIPS, 2023)

Introducing Model Inversion Attacks on Automatic Speaker Recognition. - Karla Pizzi; Franziska Boenisch; Ugur Sahin; Konstantin Böttinger (CoRR, 2023)

Is Federated Learning a Practical PET Yet? - Franziska Boenisch; Adam Dziedzic; Roei Schuster; Ali Shahin Shamsabadi; Ilia Shumailov; Nicolas Papernot (CoRR, 2023)

Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility. - Mohammad Yaghini; Patty Liu; Franziska Boenisch; Nicolas Papernot (CoRR, 2023)

Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees. - Franziska Boenisch; Christopher Mühl; Roy Rinberg; Jannis Ihrig; Adam Dziedzic (Proc. Priv. Enhancing Technol., 2023)

A Unified Framework for Quantifying Privacy Risk in Synthetic Data. - Matteo Giomi; Franziska Boenisch; Christoph Wehmeyer; Borbála Tasnádi (Proc. Priv. Enhancing Technol., 2023)

Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks. - Marcin Podhajski; Jan Dubinski; Franziska Boenisch; Adam Dziedzic; Agnieszka Pregowska; Tomasz P. Michalak (ECAI, 2024)

Memorization in Self-Supervised Learning Improves Downstream Generalization. - Wenhao Wang; Muhammad Ahmad Kaleem; Adam Dziedzic; Michael Backes 001; Nicolas Papernot; Franziska Boenisch (ICLR, 2024)

Controlled privacy leakage propagation throughout differential private overlapping grouped learning. - Shahrzad Kiani; Franziska Boenisch; Stark C. Draper (ISIT, 2024)

Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives. - Vincent Hanke; Tom Blanchard; Franziska Boenisch; Iyiola E. Olatunji; Michael Backes 001; Adam Dziedzic (NeurIPS, 2024)

Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models. - Dominik Hintersdorf; Lukas Struppek; Kristian Kersting; Adam Dziedzic; Franziska Boenisch (NeurIPS, 2024)

Localizing Memorization in SSL Vision Encoders. - Wenhao Wang; Adam Dziedzic; Michael Backes 001; Franziska Boenisch (NeurIPS, 2024)

Personalized Differential Privacy for Ridge Regression. - Krishna Acharya; Franziska Boenisch; Rakshit Naidu; Juba Ziani (CoRR, 2024)

Regulation Games for Trustworthy Machine Learning. - Mohammad Yaghini; Patty Liu; Franziska Boenisch; Nicolas Papernot (CoRR, 2024)

Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing. - Yihan Wang; Yiwei Lu 001; Guojun Zhang; Franziska Boenisch; Adam Dziedzic; Yaoliang Yu; Xiao-Shan Gao (CoRR, 2024)

Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning. - Dariush Wahdany; Matthew Jagielski; Adam Dziedzic; Franziska Boenisch (CoRR, 2024)

Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks. - Antoni Kowalczuk; Jan Dubinski; Atiyeh Ashari Ghomi; Yi Sui 001; George Stein; Jiapeng Wu; Jesse C. Cresswell; Franziska Boenisch; Adam Dziedzic (CoRR, 2024)

On the Privacy Risk of In-context Learning. - Haonan Duan 002; Adam Dziedzic; Mohammad Yaghini; Nicolas Papernot; Franziska Boenisch (CoRR, 2024)

Controlled Privacy Leakage Propagation Throughout Overlapping Grouped Learning. - Shahrzad Kiani; Franziska Boenisch; Stark C. Draper (IEEE J. Sel. Areas Inf. Theory, 2024)

From Differential Privacy to Bounds on Membership Inference: Less can be More. - Anvith Thudi; Ilia Shumailov; Franziska Boenisch; Nicolas Papernot (Trans. Mach. Learn. Res., 2024)

Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. - Jiapeng Wu; Atiyeh Ashari Ghomi; David Glukhov; Jesse C. Cresswell; Franziska Boenisch; Nicolas Papernot (Trans. Mach. Learn. Res., 2024)

Differentially Private Prototypes for Imbalanced Transfer Learning. - Dariush Wahdany; Matthew Jagielski; Adam Dziedzic; Franziska Boenisch (AAAI, 2025)

CDI: Copyrighted Data Identification in Diffusion Models. - Jan Dubinski; Antoni Kowalczuk; Franziska Boenisch; Adam Dziedzic (CVPR, 2025)

Differentially Private Federated Learning with Time-Adaptive Privacy Spending. - Shahrzad Kiani; Nupur Kulkarni; Adam Dziedzic; Stark C. Draper; Franziska Boenisch (ICLR, 2025)

Precise Parameter Localization for Textual Generation in Diffusion Models. - Lukasz Staniszewski; Bartosz Cywinski; Franziska Boenisch; Kamil Deja; Adam Dziedzic (ICLR, 2025)

Captured by Captions: On Memorization and its Mitigation in CLIP Models. - Wenhao Wang; Adam Dziedzic; Grace C. Kim; Michael Backes 001; Franziska Boenisch (ICLR, 2025)

A self-portrayal of GI Junior Fellow Franziska Boenisch: trustworthy machine learning for individuals. - Franziska Boenisch (it Inf. Technol., 2025)

MUC: Machine Unlearning for Contrastive Learning with Black-box Evaluation. - Yihan Wang; Yiwei Lu 001; Guojun Zhang; Franziska Boenisch; Adam Dziedzic; Yaoliang Yu; Xiao-Shan Gao (Trans. Mach. Learn. Res., 2025)

Privacy Attacks on Image AutoRegressive Models. - Antoni Kowalczuk; Jan Dubinski; Franziska Boenisch; Adam Dziedzic (CoRR, 2025)

Beautiful Images, Toxic Words: Understanding and Addressing Offensive Text in Generated Images. - Aditya Kumar; Tom Blanchard; Adam Dziedzic; Franziska Boenisch (CoRR, 2025)

Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models. - Jamie Hayes; Ilia Shumailov; Christopher A. Choquette-Choo; Matthew Jagielski; Georgios Kaissis; Katherine Lee; Milad Nasr; Sahra Ghalebikesabi; Niloofar Mireshghallah; Meenatchi Sundaram Mutu Selva Annamalai; Igor Shilov; Matthieu Meeus; et al. (CoRR, 2025)

Unlocking Post-hoc Dataset Inference with Synthetic Data. - Bihe Zhao; Pratyush Maini; Franziska Boenisch; Adam Dziedzic (CoRR, 2025)

Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs. - Xun Wang; Jing Xu; Franziska Boenisch; Michael Backes 001; Christopher A. Choquette-Choo; Adam Dziedzic (CoRR, 2025)

BitMark for Infinity: Watermarking Bitwise Autoregressive Image Generative Models. - Louis Kerner; Michel Meintz; Bihe Zhao; Franziska Boenisch; Adam Dziedzic (CoRR, 2025)

Radioactive Watermarks in Diffusion and Autoregressive Image Generative Models. - Michel Meintz; Jan Dubinski; Franziska Boenisch; Adam Dziedzic (CoRR, 2025)

Implementing Adaptations for Vision AutoRegressive Model. - Kaif Shaikh; Franziska Boenisch; Adam Dziedzic (CoRR, 2025)

Finding Dori: Memorization in Text-to-Image Diffusion Models Is Less Local Than Assumed. - Antoni Kowalczuk; Dominik Hintersdorf; Lukas Struppek; Kristian Kersting; Adam Dziedzic; Franziska Boenisch (CoRR, 2025)

Adversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs). - Iyiola E. Olatunji; Franziska Boenisch; Jing Xu; Adam Dziedzic (CoRR, 2025)

Demystifying Foreground-Background Memorization in Diffusion Models. - Jimmy Z. Di; Yiwei Lu 001; Yaoliang Yu; Gautam Kamath 001; Adam Dziedzic; Franziska Boenisch (CoRR, 2025)