Franziska Boenisch

According to our database, Franziska Boenisch authored at least 42 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Augment then Smooth: Reconciling Differential Privacy with Certified Robustness.

Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot

Trans. Mach. Learn. Res., 2024

From Differential Privacy to Bounds on Membership Inference: Less can be More.

Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot

Trans. Mach. Learn. Res., 2024

Controlled Privacy Leakage Propagation Throughout Overlapping Grouped Learning.

Shahrzad Kiani, Franziska Boenisch, Stark C. Draper

IEEE J. Sel. Areas Inf. Theory, 2024

CDI: Copyrighted Data Identification in Diffusion Models.

Jan Dubinski, Antoni Kowalczuk, Franziska Boenisch, Adam Dziedzic

CoRR, 2024

On the Privacy Risk of In-context Learning.

Haonan Duan, Adam Dziedzic, Mohammad Yaghini, Nicolas Papernot, Franziska Boenisch

CoRR, 2024

Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives.

Vincent Hanke, Tom Blanchard, Franziska Boenisch, Iyiola E. Olatunji, Michael Backes, Adam Dziedzic

CoRR, 2024

Localizing Memorization in SSL Vision Encoders.

Wenhao Wang, Adam Dziedzic, Michael Backes, Franziska Boenisch

CoRR, 2024

Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks.

Antoni Kowalczuk, Jan Dubinski, Atiyeh Ashari Ghomi, Yi Sui, George Stein, Jiapeng Wu, Jesse C. Cresswell, Franziska Boenisch, Adam Dziedzic

CoRR, 2024

Beyond the Mean: Differentially Private Prototypes for Private Transfer Learning.

Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch

CoRR, 2024

Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing.

Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao

CoRR, 2024

Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models.

Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, Franziska Boenisch

CoRR, 2024

Regulation Games for Trustworthy Machine Learning.

Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot

CoRR, 2024

Personalized Differential Privacy for Ridge Regression.

Krishna Acharya, Franziska Boenisch, Rakshit Naidu, Juba Ziani

CoRR, 2024

Controlled privacy leakage propagation throughout differential private overlapping grouped learning.

Shahrzad Kiani, Franziska Boenisch, Stark C. Draper

Proceedings of the IEEE International Symposium on Information Theory, 2024

Memorization in Self-Supervised Learning Improves Downstream Generalization.

Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic, Michael Backes, Nicolas Papernot, Franziska Boenisch

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks.

Marcin Podhajski, Jan Dubinski, Franziska Boenisch, Adam Dziedzic, Agnieszka Pregowska, Tomasz P. Michalak

Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

2023

A Unified Framework for Quantifying Privacy Risk in Synthetic Data.

Matteo Giomi, Franziska Boenisch, Christoph Wehmeyer, Borbála Tasnádi

Proc. Priv. Enhancing Technol., April, 2023

Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees.

Franziska Boenisch, Christopher Mühl, Roy Rinberg, Jannis Ihrig, Adam Dziedzic

Proc. Priv. Enhancing Technol., January, 2023

Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility.

Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot

CoRR, 2023

Is Federated Learning a Practical PET Yet?

Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot

CoRR, 2023

Introducing Model Inversion Attacks on Automatic Speaker Recognition.

Karla Pizzi, Franziska Boenisch, Ugur Sahin, Konstantin Böttinger

CoRR, 2023

Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders.

Jan Dubinski, Stanislaw Pawlak, Franziska Boenisch, Tomasz Trzcinski, Adam Dziedzic

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Flocks of Stochastic Parrots: Differentially Private Prompt Learning for Large Language Models.

Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Have it your way: Individualized Privacy Assignment for DP-SGD.

Franziska Boenisch, Christopher Mühl, Adam Dziedzic, Roy Rinberg, Nicolas Papernot

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation.

Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot

Proceedings of the 8th IEEE European Symposium on Security and Privacy, 2023

When the Curious Abandon Honesty: Federated Learning Is Not Private.

Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot

Proceedings of the 8th IEEE European Symposium on Security and Privacy, 2023

2022

Secure and Private Machine Learning.

Franziska Boenisch

PhD thesis, 2022

Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks.

Tabea Kossen, Manuel A. Hirzel, Vince I. Madai, Franziska Boenisch, Anja Hennemuth, Kristian Hildebrand, Sebastian Pokutta, Kartikey Sharma, Adam Hilbert, Jan Sobesky, Ivana Galinovic, Ahmed A. Khalil, Jochen B. Fiebach, Dietmar Frey

Frontiers Artif. Intell., 2022

Bounding Membership Inference.

Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot

CoRR, 2022

Personalized PATE: Differential Privacy for Machine Learning with Individual Privacy Guarantees.

Christopher Mühl, Franziska Boenisch

CoRR, 2022

Dataset Inference for Self-Supervised Models.

Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The Influence of Training Parameters on Neural Networks' Vulnerability to Membership Inference Attacks.

Oussama Bouanani, Franziska Boenisch

Proceedings of the 52. Jahrestagung der Gesellschaft für Informatik, INFORMATIK 2022, Informatik in den Naturwissenschaften, 26., 2022

2021

A Systematic Review on Model Watermarking for Neural Networks.

Franziska Boenisch

Frontiers Big Data, 2021

Privatsphäre und Maschinelles Lernen.

Franziska Boenisch

Datenschutz und Datensicherheit, 2021

Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning.

Franziska Boenisch, Philip Sperl, Konstantin Böttinger

CoRR, 2021

Privacy Needs Reflection: Conceptional Design Rationales for Privacy-Preserving Explanation User Interfaces.

Peter Sörries, Claudia Müller-Birn, Katrin Glinka, Franziska Boenisch, Marian Margraf, Sabine Sayegh-Jodehl, Matthias Rose

Proceedings of the Mensch und Computer 2021, 2021

"I Never Thought About Securing My Machine Learning Systems": A Study of Security and Privacy Awareness of Machine Learning Practitioners.

Franziska Boenisch, Verena Battis, Nicolas Buchmann, Maija Poikela

Proceedings of the MuC '21: Mensch und Computer 2021, 2021

Side-Channel Attacks on Query-Based Data Anonymization.

Franziska Boenisch, Reinhard Munz, Marcel Tiepelt, Simon Hanisch, Christiane Kuhn, Paul Francis

Proceedings of the CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea, November 15, 2021

2020

A Survey on Model Watermarking Neural Networks.

Franziska Boenisch

CoRR, 2020

2019

Applying Differential Privacy to Machine Learning: Challenges and Potentials.

Franziska Boenisch

Proceedings of the 31. Krypto-Tag, Berlin, Germany, October 17-18, 2019, 2019

2018

Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence.

Franziska Boenisch, Benjamin Rosemann, Benjamin Wild, David M. Dormagen, Fernando Wario, Tim Landgraf

Frontiers Robotics AI, 2018

Tracking all members of a honey bee colony over their lifetime.

Franziska Boenisch, Benjamin Rosemann, Benjamin Wild, Fernando Wario, David M. Dormagen, Tim Landgraf

CoRR, 2018