Franziska Boenisch

Affiliations:

CISPA Helmholtz Center for Information Security, Germany

According to our database¹, Franziska Boenisch authored at least 30 papers between 2018 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

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In proceedings

Article

PhD thesis

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Other

Bibliography

2024

Regulation Games for Trustworthy Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2024

Personalized Differential Privacy for Ridge Regression.

[BibT_eX]

[DOI]

CoRR, 2024

Memorization in Self-Supervised Learning Improves Downstream Generalization.

[BibT_eX]

[DOI]

Wenhao Wang

Muhammad Ahmad Kaleem

CoRR, 2024

2023

A Unified Framework for Quantifying Privacy Risk in Synthetic Data.

[BibT_eX]

[DOI]

Proc. Priv. Enhancing Technol., April, 2023

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

[BibT_eX]

[DOI]

Proc. Priv. Enhancing Technol., January, 2023

Augment then Smooth: Reconciling Differential Privacy with Certified Robustness.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

Is Federated Learning a Practical PET Yet?

[BibT_eX]

[DOI]

Franziska Boenisch

Adam Dziedzic

Roei Schuster

Ali Shahin Shamsabadi

Ilia Shumailov

Nicolas Papernot

CoRR, 2023

Introducing Model Inversion Attacks on Automatic Speaker Recognition.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Franziska Boenisch

PhD thesis, 2022

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

[BibT_eX]

[DOI]

Frontiers Artif. Intell., 2022

Bounding Membership Inference.

[BibT_eX]

[DOI]

CoRR, 2022

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

[BibT_eX]

[DOI]

Christopher Mühl

Franziska Boenisch

CoRR, 2022

Dataset Inference for Self-Supervised Models.

[BibT_eX]

[DOI]

Adam Dziedzic

Haonan Duan

Muhammad Ahmad Kaleem

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Franziska Boenisch

Frontiers Big Data, 2021

Privatsphäre und Maschinelles Lernen.

[BibT_eX]

[DOI]

Franziska Boenisch

Datenschutz und Datensicherheit, 2021

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

[BibT_eX]

[DOI]

Franziska Boenisch

Philip Sperl

Konstantin Böttinger

CoRR, 2021

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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

Side-Channel Attacks on Query-Based Data Anonymization.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Franziska Boenisch

CoRR, 2020

2019

Applying Differential Privacy to Machine Learning: Challenges and Potentials.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Frontiers Robotics AI, 2018

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

[BibT_eX]

[DOI]

CoRR, 2018

Franziska Boenisch

Timeline

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