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ORCIDFranziska Boenisch
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- affiliation: CISPA Helmholtz Center for Information Security, Germany
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Books and Theses
- 2022
[b1]Franziska Boenisch:
Secure and Private Machine Learning. Free University of Berlin, Germany, 2022
Journal Articles
- 2024
- [j9]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) - [j8]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) - [j7]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) - 2023
- [j6]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) - [j5]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) - 2022
- [j4]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) - 2021
- [j3]Franziska Boenisch:
Privatsphäre und Maschinelles Lernen. Datenschutz und Datensicherheit 45(7): 448-452 (2021) - [j2]Franziska Boenisch:
A Systematic Review on Model Watermarking for Neural Networks. Frontiers Big Data 4: 729663 (2021) - 2018
- [j1]Franziska Boenisch, Benjamin Rosemann, Benjamin Wild, David M. 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: 35 (2018)
Conference and Workshop Papers
- 2024
- [c14]Marcin Podhajski, Jan Dubinski, Franziska Boenisch, Adam Dziedzic, Agnieszka Pregowska, Tomasz P. Michalak:
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks. ECAI 2024: 1438-1445 - [c13]Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch:
Memorization in Self-Supervised Learning Improves Downstream Generalization. ICLR 2024 - [c12]Shahrzad Kiani, Franziska Boenisch, Stark C. Draper:
Controlled privacy leakage propagation throughout differential private overlapping grouped learning. ISIT 2024: 386-391 - 2023
- [c11]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 - [c10]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 - [c9]Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot:
Have it your way: Individualized Privacy Assignment for DP-SGD. NeurIPS 2023 - [c8]Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch:
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models. NeurIPS 2023 - [c7]Jan Dubinski, Stanislaw Pawlak, Franziska Boenisch, Tomasz Trzcinski, Adam Dziedzic:
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders. NeurIPS 2023 - 2022
- [c6]Oussama Bouanani, Franziska Boenisch:
The Influence of Training Parameters on Neural Networks' Vulnerability to Membership Inference Attacks. GI-Jahrestagung 2022: 1227-1246 - [c5]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 - 2021
- [c4]Franziska Boenisch, Reinhard Munz, Marcel Tiepelt, Simon Hanisch, Christiane Kuhn, Paul Francis:
Side-Channel Attacks on Query-Based Data Anonymization. CCS 2021: 1254-1265 - [c3]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. MuC 2021: 520-546 - [c2]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 - 2019
- [c1]Franziska Boenisch:
Applying Differential Privacy to Machine Learning: Challenges and Potentials. Krypto-Tag 2019
Informal and Other Publications
- 2024
- [i24]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) - [i23]Krishna Acharya, Franziska Boenisch, Rakshit Naidu, Juba Ziani:
Personalized Differential Privacy for Ridge Regression. CoRR abs/2401.17127 (2024) - [i22]Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot:
Regulation Games for Trustworthy Machine Learning. CoRR abs/2402.03540 (2024) - [i21]Marcin Podhajski, Jan Dubinski, Franziska Boenisch, Adam Dziedzic, Agnieszka Pregowska, Tomasz P. Michalak:
Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks. CoRR abs/2405.12295 (2024) - [i20]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) - [i19]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) - [i18]Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch:
Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning. CoRR abs/2406.08039 (2024) - [i17]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) - [i16]Wenhao Wang, Adam Dziedzic, Michael Backes, Franziska Boenisch:
Localizing Memorization in SSL Vision Encoders. CoRR abs/2409.19069 (2024) - 2023
- [i15]Karla Pizzi, Franziska Boenisch, Ugur Sahin, Konstantin Böttinger:
Introducing Model Inversion Attacks on Automatic Speaker Recognition. CoRR abs/2301.03206 (2023) - [i14]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) - [i13]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) - [i12]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) - [i11]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) - [i10]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) - [i9]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
- [i8]Christopher Mühl, Franziska Boenisch:
Personalized PATE: Differential Privacy for Machine Learning with Individual Privacy Guarantees. CoRR abs/2202.10517 (2022) - [i7]Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot:
Bounding Membership Inference. CoRR abs/2202.12232 (2022) - [i6]Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot:
Dataset Inference for Self-Supervised Models. CoRR abs/2209.09024 (2022) - [i5]Matteo Giomi, Franziska Boenisch, Christoph Wehmeyer, Borbála Tasnádi:
A Unified Framework for Quantifying Privacy Risk in Synthetic Data. CoRR abs/2211.10459 (2022) - 2021
- [i4]Franziska Boenisch, Philip Sperl, Konstantin Böttinger:
Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning. CoRR abs/2105.07985 (2021) - [i3]Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot:
When the Curious Abandon Honesty: Federated Learning Is Not Private. CoRR abs/2112.02918 (2021) - 2020
- [i2]Franziska Boenisch:
A Survey on Model Watermarking Neural Networks. CoRR abs/2009.12153 (2020) - 2018
- [i1]Franziska Boenisch, Benjamin Rosemann, Benjamin Wild, Fernando Wario, David M. Dormagen, Tim Landgraf:
Tracking all members of a honey bee colony over their lifetime. CoRR abs/1802.03192 (2018)
Coauthor Index
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