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 | 135* | 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 | 134* | 2023 |
A Systematic Review on Model Watermarking for Neural Networks F Boenisch Frontiers in Big Data 4, 96, 2021 | 84 | 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 | 58 | 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 | 26 | 2023 |
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 | 22 | 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 | 21 | 2021 |
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 | 17 | 2021 |
Bounding Membership Inference A Thudi, I Shumailov, F Boenisch, N Papernot arXiv preprint arXiv:2202.12232, 2022 | 16 | 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 | 14 | 2023 |
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 | 13 | 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 | 13 | 2022 |
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 | 10* | 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 | 10 | 2021 |
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 | 9 | 2022 |
Privatsphäre und Maschinelles Lernen: Über Gefahren und Schutzmaßnahmen F Boenisch Datenschutz und Datensicherheit-DuD 45, 448-452, 2021 | 8 | 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 | 5 | 2021 |
Feature engineering and probabilistic tracking on honey bee trajectories F Boenisch Bachelor thesis, Freie Universität Berlin, 2017 | 5 | 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 | 4 | 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 | 3* | 2023 |