Publications
This page provides an overview on my publications.
Author(s) | Year | Title and Publication | Link |
---|---|---|---|
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov and Nicolas Papernot | 2023 | Is Federated Learning a Practical PET Yet?. arXiv preprint arXiv:2301.04017 | here |
Matteo Giomi, Franziska Boenisch, Christoph Wehmeyer, and Borbála Tasnádi | 2022 | A Unified Framework for Quantifying Privacy Risk in Synthetic Data. 23rd Privacy Enhancing Technologies Symposium (PoPETs ‘23) | here |
Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, and Nicolas Papernot | 2022 | Dataset Inference for Self-Supervised Models. Neural Information Processing Systems (NeurIPS ‘22) | here |
Franziska Boenisch, Christopher Mühl, Roy Rinberg, Jannis Ihrig, and Adam Dziedzic | 2022 | Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees. 23rd Privacy Enhancing Technologies Symposium (PoPETs ‘23) | here |
Karla Pizzi, Franziska Boenisch, Ugur Sahin, and Konstantin Böttinger | 2022 | Introducing Model Inversion Attacks on Automatic Speaker Recognition. Proc. 2nd Symposium on Security and Privacy in Speech Communication | here |
Tabea Kossen, Manuel Hirzel, Vince Madai, Franziska Boenisch, Anja Hennemuth, Kristian Hildebrand, Sebastian Pokutta, Kartikey Sharma, Adam Hilbert, Jan Sobesky, Ivana Galinovich, Ahmed Khalil, Jochen Fiebach, and Dietmar Frey. | 2022 | Towards sharing brain images: Differentially private TOF-MRA images with segmentation labels using generative adversarial networks. Frontiers in Artificial Intelligence | here |
Anvith Thudi, Ilia Shumailov, Franziska Boenisch, and Nicolas Papernot | 2021 | Bounding Membership Inference. arXiv preprint arXiv:2202.12232 | here |
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov and Nicolas Papernot | 2021 | When the Curious Abandon Honesty: Federated Learning Is Not Private. arXiv preprint arXiv:2112.02918 | here |
Franziska Boenisch | 2021 | A Systematic Review on Model Watermarking for Neural Networks. Frontiers in Big Data, 4(96). | here |
Franziska Boenisch, Reinhard Munz, Marcel Tiepelt, Simon Hanisch, Christiane Kuhn, and Paul Francis | 2021 | Side-Channel Attacks on Query-Based Data Anonymization. Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security (CCS’21), November15–19,2021,Virtual Event, Republic of Korea | here |
Franziska Boenisch, Verena Battis, Nicolas Buchmann, and Maija Poikela | 2021 | “I Never Thought About Securing My Machine Learning Systems”: A Study of Security and Privacy Awareness of Machine Learning Practitioners Mensch und Computer 2021, 520-546. | here |
Sörries, Peter, Claudia Müller-Birn, Katrin Glinka, Franziska Boenisch, Marian Margraf, Sabine Sayegh-Jodehl, and Matthias Rose | 2021 | Privacy Needs Reflection: Conceptional Design Rationales for Privacy-Preserving Explanation User Interfaces. Mensch und Computer 2021, Workshow-Proceedings. | here |
Franziska Boenisch | 2021 | Privatsphäre und Maschinelles Lernen. Datenschutz Datensicherheit 45, 448–452. | here |
Franziska Boenisch, Philip Sperl, and Konstantin Böttinger | 2021 | Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning. arXiv preprint arXiv:2105.07985 | here |
Franziska Boenisch, Benjamin Rosemann, Benjamin Wild, David Dormagen, Fernando Wario, and Tim Landgraf | 2018 | Tracking all members of a honey bee colony over their lifetime using learned models of correspondence. Frontiers in Robotics and AI. 5(35). | here |