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


Feb 17, 2023
Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot

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Is Federated Learning a Practical PET Yet?


Jan 09, 2023
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot

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Introducing Model Inversion Attacks on Automatic Speaker Recognition


Jan 09, 2023
Karla Pizzi, Franziska Boenisch, Ugur Sahin, Konstantin Böttinger

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* Proc. 2nd Symposium on Security and Privacy in Speech Communication, 2022 
* for associated pdf, see https://www.isca-speech.org/archive/pdfs/spsc_2022/pizzi22_spsc.pdf 

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Dataset Inference for Self-Supervised Models


Sep 16, 2022
Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot

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* Accepted at NeurIPS 2022 

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Bounding Membership Inference


Feb 24, 2022
Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot

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


Feb 23, 2022
Christopher Mühl, Franziska Boenisch

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When the Curious Abandon Honesty: Federated Learning Is Not Private


Dec 06, 2021
Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov, Nicolas Papernot

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


May 17, 2021
Franziska Boenisch, Philip Sperl, Konstantin Böttinger

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A Survey on Model Watermarking Neural Networks


Sep 25, 2020
Franziska Boenisch

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