Franziska Boenisch

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 44

Top Topics

Speaker Recognition

Diffusion Models

Differential Privacy

Top Venues

Proc. Priv. Enhancing Technol.

Publications

Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot
Regulation Games for Trustworthy Machine Learning. CoRR (2024)
Marcin Podhajski, Jan Dubinski, Franziska Boenisch, Adam Dziedzic, Agnieszka Pregowska, Tomasz Michalak
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks. CoRR (2024)
Krishna Acharya, Franziska Boenisch, Rakshit Naidu, Juba Ziani
Personalized Differential Privacy for Ridge Regression. CoRR (2024)
Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. Trans. Mach. Learn. Res. 2024 (2024)
Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot
From Differential Privacy to Bounds on Membership Inference: Less can be More. Trans. Mach. Learn. Res. 2024 (2024)
Shahrzad Kiani, Franziska Boenisch, Stark C. Draper
Controlled Privacy Leakage Propagation Throughout Overlapping Grouped Learning. IEEE J. Sel. Areas Inf. Theory 5 (2024)
Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao
Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing. CoRR (2024)
Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, Franziska Boenisch
Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models. CoRR (2024)
Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch
Memorization in Self-Supervised Learning Improves Downstream Generalization. CoRR (2024)
Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch
Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning. CoRR (2024)
Antoni Kowalczuk, Jan Dubinski, Atiyeh Ashari Ghomi, Yi Sui, George Stein, Jiapeng Wu, Jesse C. Cresswell, Franziska Boenisch, Adam Dziedzic
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks. CoRR (2024)
Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch
Memorization in Self-Supervised Learning Improves Downstream Generalization. ICLR (2024)
Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. CoRR (2023)
Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. NeurIPS (2023)
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
When the Curious Abandon Honesty: Federated Learning Is Not Private. EuroS&P (2023)
Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot
Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility. CoRR (2023)
Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. CoRR (2023)
Jan Dubinski, Stanislaw Pawlak, Franziska Boenisch, Tomasz Trzcinski, Adam Dziedzic
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. CoRR (2023)
Jan Dubinski, Stanislaw Pawlak, Franziska Boenisch, Tomasz Trzcinski, Adam Dziedzic
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. NeurIPS (2023)
Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot
Have it your way: Individualized Privacy Assignment for DP-SGD. CoRR (2023)
Matteo Giomi, Franziska Boenisch, Christoph Wehmeyer, Borbála Tasnádi
A Unified Framework for Quantifying Privacy Risk in Synthetic Data. Proc. Priv. Enhancing Technol. 2023 (2) (2023)
Karla Pizzi, Franziska Boenisch, Ugur Sahin, Konstantin Böttinger
Introducing Model Inversion Attacks on Automatic Speaker Recognition. CoRR (2023)
Franziska Boenisch, Christopher Mühl, Roy Rinberg, Jannis Ihrig, Adam Dziedzic
Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees. Proc. Priv. Enhancing Technol. 2023 (1) (2023)
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
Is Federated Learning a Practical PET Yet? CoRR (2023)
Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot
Have it your way: Individualized Privacy Assignment for DP-SGD. NeurIPS (2023)
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation. EuroS&P (2023)
Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot
Bounding Membership Inference. CoRR (2022)
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, Jochen B. Fiebach, Dietmar Frey
Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks. Frontiers Artif. Intell. 5 (2022)
Matteo Giomi, Franziska Boenisch, Christoph Wehmeyer, Borbála Tasnádi
A Unified Framework for Quantifying Privacy Risk in Synthetic Data. CoRR (2022)
Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot
Dataset Inference for Self-Supervised Models. NeurIPS (2022)
Oussama Bouanani, Franziska Boenisch
The Influence of Training Parameters on Neural Networks' Vulnerability to Membership Inference Attacks. GI-Jahrestagung (2022)
Christopher Mühl, Franziska Boenisch
Personalized PATE: Differential Privacy for Machine Learning with Individual Privacy Guarantees. CoRR (2022)
Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot
Dataset Inference for Self-Supervised Models. CoRR (2022)
Franziska Boenisch, Philip Sperl, Konstantin Böttinger
Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning. CoRR (2021)
Peter Sörries, Claudia Müller-Birn, Katrin Glinka, Franziska Boenisch, Marian Margraf, Sabine Sayegh-Jodehl, Matthias Rose
Privacy Needs Reflection: Conceptional Design Rationales for Privacy-Preserving Explanation User Interfaces. MuC (Workshopband) (2021)
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot
When the Curious Abandon Honesty: Federated Learning Is Not Private. CoRR (2021)
Franziska Boenisch
A Systematic Review on Model Watermarking for Neural Networks. Frontiers Big Data 4 (2021)
Franziska Boenisch, Reinhard Munz, Marcel Tiepelt, Simon Hanisch, Christiane Kuhn, Paul Francis
Side-Channel Attacks on Query-Based Data Anonymization. CCS (2021)
Franziska Boenisch
Privatsphäre und Maschinelles Lernen. Datenschutz und Datensicherheit 45 (7) (2021)
Franziska Boenisch, Verena Battis, Nicolas Buchmann, Maija Poikela
"I Never Thought About Securing My Machine Learning Systems": A Study of Security and Privacy Awareness of Machine Learning Practitioners. Mensch und Computer (2021)
Franziska Boenisch
A Survey on Model Watermarking Neural Networks. CoRR (2020)
Franziska Boenisch
Applying Differential Privacy to Machine Learning: Challenges and Potentials. Krypto-Tag (2019)
Franziska Boenisch, Benjamin Rosemann, Benjamin Wild, Fernando Wario, David Dormagen, Tim Landgraf
Tracking all members of a honey bee colony over their lifetime. CoRR (2018)
Franziska Boenisch, Benjamin Rosemann, Benjamin Wild, David Dormagen, Fernando Wario, Tim Landgraf
Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence. Frontiers Robotics AI 5 (2018)