Franziska Boenisch

Franziska Boenisch

Postdoctoral Fellow
@ Vector Institute

Research focus:
Privacy-Preserving and Trustworthy ML.

Publications

This page provides an overview on my publications.

Author(s)YearTitle and PublicationLink
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov and Nicolas Papernot2023Is Federated Learning a Practical PET Yet?.
arXiv preprint arXiv:2301.04017		here
Matteo Giomi, Franziska Boenisch, Christoph Wehmeyer, and Borbála Tasnádi2022A 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 Papernot2022Dataset Inference for Self-Supervised Models.
Neural Information Processing Systems (NeurIPS ‘22)		here
Franziska Boenisch, Christopher Mühl, Roy Rinberg, Jannis Ihrig, and Adam Dziedzic2022Individualized 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 Böttinger2022Introducing 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.2022Towards 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 Papernot2021Bounding Membership Inference.
arXiv preprint arXiv:2202.12232		here
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov and Nicolas Papernot2021When the Curious Abandon Honesty: Federated Learning Is Not Private.
arXiv preprint arXiv:2112.02918		here
Franziska Boenisch2021A 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 Francis2021Side-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 Poikela2021“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 Rose2021Privacy Needs Reflection: Conceptional Design Rationales for Privacy-Preserving Explanation User Interfaces.
Mensch und Computer 2021, Workshow-Proceedings.		here
Franziska Boenisch2021Privatsphäre und Maschinelles Lernen.
Datenschutz Datensicherheit 45, 448–452.		here
Franziska Boenisch, Philip Sperl, and Konstantin Böttinger2021Gradient 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 Landgraf2018Tracking all members of a honey bee colony over their lifetime using learned models of correspondence.
Frontiers in Robotics and AI. 5(35).		here