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- Franziska Boenisch
CISPA Helmholtz Center for Information Security, Germany
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Publication search results
found 46 matches
- 2024
- Shahrzad Kiani, Franziska Boenisch, Stark C. Draper:
Controlled Privacy Leakage Propagation Throughout Overlapping Grouped Learning. IEEE J. Sel. Areas Inf. Theory 5: 442-463 (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) - 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) - Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch:
Memorization in Self-Supervised Learning Improves Downstream Generalization. ICLR 2024 - Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch:
Memorization in Self-Supervised Learning Improves Downstream Generalization. CoRR abs/2401.12233 (2024) - Krishna Acharya, Franziska Boenisch, Rakshit Naidu, Juba Ziani:
Personalized Differential Privacy for Ridge Regression. CoRR abs/2401.17127 (2024) - Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot:
Regulation Games for Trustworthy Machine Learning. CoRR abs/2402.03540 (2024) - Marcin Podhajski, Jan Dubinski, Franziska Boenisch, Adam Dziedzic, Agnieszka Pregowska, Tomasz Michalak:
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks. CoRR abs/2405.12295 (2024) - Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, Franziska Boenisch:
Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models. CoRR abs/2406.02366 (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 abs/2406.03603 (2024) - Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch:
Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning. CoRR abs/2406.08039 (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 abs/2407.12588 (2024) - 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): 158-176 (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): 312-328 (2023) - Tobias Buhl, David Marcomin, Stefan Fallert, Jana Blechschmidt, Franziska Bönisch, Robert Mark, Juliano Sarmento Cabral, Sebastian von Mammen:
User-Centered Engineering of an Interactive Land Use Exploration Tool. EnvirVis@EuroVis 2023: 77-84 - 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: 175-199 - 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: 241-257 - Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot:
Have it your way: Individualized Privacy Assignment for DP-SGD. NeurIPS 2023 - Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch:
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. NeurIPS 2023 - Jan Dubinski, Stanislaw Pawlak, Franziska Boenisch, Tomasz Trzcinski, Adam Dziedzic:
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. NeurIPS 2023 - Karla Pizzi, Franziska Boenisch, Ugur Sahin, Konstantin Böttinger:
Introducing Model Inversion Attacks on Automatic Speaker Recognition. CoRR abs/2301.03206 (2023) - Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
Is Federated Learning a Practical PET Yet? CoRR abs/2301.04017 (2023) - Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot:
Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility. CoRR abs/2302.09183 (2023) - Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot:
Have it your way: Individualized Privacy Assignment for DP-SGD. CoRR abs/2303.17046 (2023) - Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch:
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. CoRR abs/2305.15594 (2023) - Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot:
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. CoRR abs/2306.08656 (2023) - Jan Dubinski, Stanislaw Pawlak, Franziska Boenisch, Tomasz Trzcinski, Adam Dziedzic:
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. CoRR abs/2310.08571 (2023) - 2022
- Franziska Boenisch:
Secure and Private Machine Learning. Free University of Berlin, Germany, 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: 813842 (2022) - Oussama Bouanani, Franziska Boenisch:
The Influence of Training Parameters on Neural Networks' Vulnerability to Membership Inference Attacks. GI-Jahrestagung 2022: 1227-1246
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