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Franziska Boenisch
Franziska Boenisch
Postdoctoral Fellow, Vector Institute
Verified email at vectorinstitute.ai - Homepage
Title
Cited by
Cited by
Year
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
42*2018
A Systematic Review on Model Watermarking for Neural Networks
F Boenisch
Frontiers in Big Data 4, 96, 2021
31*2021
When the Curious Abandon Honesty: Federated Learning Is Not Private
F Boenisch, A Dziedzic, R Schuster, AS Shamsabadi, I Shumailov, ...
arXiv preprint arXiv:2112.02918, 2021
72021
Feature engineering and probabilistic tracking on honey bee trajectories
F Boenisch
Bachelor thesis, Freie Universität Berlin, 2017
62017
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
42021
Bounding Membership Inference
A Thudi, I Shumailov, F Boenisch, N Papernot
arXiv preprint arXiv:2202.12232, 2022
32022
“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
32021
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
32021
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
22021
Privatsphäre und Maschinelles Lernen
F Boenisch
Datenschutz und Datensicherheit-DuD 45 (7), 448-452, 2021
22021
Personalized PATE: Differential Privacy for Machine Learning with Individual Privacy Guarantees
C Mühl, F Boenisch
arXiv preprint arXiv:2202.10517, 2022
12022
Differential Privacy: General Survey and Analysis of Practicability in the Context of Machine Learning
F Boenisch
Master Thesis, Freie Universität Berlin, 2019
12019
Dataset Inference for Self-Supervised Models
A Dziedzic, H Duan, MA Kaleem, N Dhawan, J Guan, Y Cattan, ...
arXiv preprint arXiv:2209.09024, 2022
2022
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, 2022
2022
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