Franziska Boenisch
Franziska Boenisch
Assistant Professor, CISPA Helmholtz Center for Information Security
Verified email at - Homepage
Cited by
Cited by
Testing robustness against unforeseen adversaries
M Kaufmann, D Kang, Y Sun, S Basart, X Yin, M Mazeika, A Arora, ...
arXiv preprint arXiv:1908.08016, 2019
When the Curious Abandon Honesty: Federated Learning Is Not Private
F Boenisch, A Dziedzic, R Schuster, AS Shamsabadi, I Shumailov, ...
Proceedings of the 8th IEEE European Symposium on Security and Privacy, 2023
A Systematic Review on Model Watermarking for Neural Networks
F Boenisch
Frontiers in Big Data 4, 96, 2021
Tracking all members of a honey bee colony over their lifetime using learned models of correspondence
F Boenisch, B Rosemann, B Wild, D Dormagen, F Wario, T Landgraf
Frontiers in Robotics and AI 5, 35, 2018
Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models
H Duan, A Dziedzic, N Papernot, F Boenisch
Advances in Neural Information Processing Systems 36, 2023
“I Never Thought About Securing My Machine Learning Systems”: A Study of Security and Privacy Awareness of Machine Learning Practitioners
F Boenisch, V Battis, N Buchmann, M Poikela
Mensch und Computer 2021, 520-546, 2021
A Unified Framework for Quantifying Privacy Risk in Synthetic Data
M Giomi, F Boenisch, C Wehmeyer, B Tasnádi
23rd Privacy Enhancing Technologies Symposium (PETs'23), 2023
Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning
F Boenisch, P Sperl, K Böttinger
arXiv preprint arXiv:2105.07985, 2021
Bounding Membership Inference
A Thudi, I Shumailov, F Boenisch, N Papernot
arXiv preprint arXiv:2202.12232, 2022
Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees
F Boenisch, C Mühl, R Rinberg, J Ihrig, A Dziedzic
23rd Privacy Enhancing Technologies Symposium (PETs'23), 2023
Dataset Inference for Self-Supervised Models
A Dziedzic, H Duan, MA Kaleem, N Dhawan, J Guan, Y Cattan, ...
NeurIPS (Neural Information Processing Systems), 2022
On the Privacy Risk of In-context Learning
H Duan, A Dziedzic, M Yaghini, N Papernot, F Boenisch
The 61st Annual Meeting Of The Association For Computational Linguistics, 2023
Side-Channel Attacks on Query-Based Data Anonymization
F Boenisch, R Munz, M Tiepelt, S Hanisch, C Kuhn, P Francis
Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications …, 2021
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation
F Boenisch, A Dziedzic, R Schuster, AS Shamsabadi, I Shumailov, ...
Proceedings of the 8th IEEE European Symposium on Security and Privacy, 2023
Toward sharing brain images: Differentially private TOF-MRA images with segmentation labels using generative adversarial networks
T Kossen, MA Hirzel, VI Madai, F Boenisch, A Hennemuth, K Hildebrand, ...
Frontiers in artificial intelligence 5, 85, 2022
Privatsphäre und Maschinelles Lernen: Über Gefahren und Schutzmaßnahmen
F Boenisch
Datenschutz und Datensicherheit-DuD 45, 448-452, 2021
Privacy Needs Reflection: Conceptional Design Rationales for Privacy-Preserving Explanation User Interfaces
P Sörries, C Müller-Birn, K Glinka, F Boenisch, M Margraf, ...
Mensch und Computer 2021-Workshopband, 2021
Feature engineering and probabilistic tracking on honey bee trajectories
F Boenisch
Bachelor thesis, Freie Universität Berlin, 2017
Have it your way: Individualized Privacy Assignment for DP-SGD
F Boenisch, C Mühl, A Dziedzic, R Rinberg, N Papernot
Advances in Neural Information Processing Systems 36, 2023
Learning to Walk Impartially on the Pareto Frontier of Fairness, Privacy, and Utility
M Yaghini, P Liu, F Boenisch, N Papernot
NeurIPS 2023 Workshop on Regulatable ML, 2023
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