Franziska Boenisch | DeepAI

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∙ 06/14/2023

Augment then Smooth: Reconciling Differential Privacy with Certified Robustness

Machine learning models are susceptible to a variety of attacks that can...

0 Jiapeng Wu, et al. ∙

∙ 05/24/2023

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

Large language models (LLMs) are excellent in-context learners. However,...

0 Haonan Duan, et al. ∙

∙ 03/29/2023

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

When training a machine learning model with differential privacy, one se...

0 Franziska Boenisch, et al. ∙

∙ 02/17/2023

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

Deploying machine learning (ML) models often requires both fairness and ...

0 Mohammad Yaghini, et al. ∙

∙ 01/09/2023

Is Federated Learning a Practical PET Yet?

Federated learning (FL) is a framework for users to jointly train a mach...

10 Franziska Boenisch, et al. ∙

∙ 01/09/2023

Introducing Model Inversion Attacks on Automatic Speaker Recognition

Model inversion (MI) attacks allow to reconstruct average per-class repr...

0 Karla Pizzi, et al. ∙

∙ 11/18/2022

A Unified Framework for Quantifying Privacy Risk in Synthetic Data

Synthetic data is often presented as a method for sharing sensitive info...

0 Matteo Giomi, et al. ∙

∙ 09/16/2022

Dataset Inference for Self-Supervised Models

Self-supervised models are increasingly prevalent in machine learning (M...

7 Adam Dziedzic, et al. ∙

∙ 02/24/2022

Bounding Membership Inference

Differential Privacy (DP) is the de facto standard for reasoning about t...

0 Anvith Thudi, et al. ∙

∙ 02/21/2022

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

Applying machine learning (ML) to sensitive domains requires privacy pro...

0 Christopher Mühl, et al. ∙

∙ 12/06/2021

When the Curious Abandon Honesty: Federated Learning Is Not Private

In federated learning (FL), data does not leave personal devices when th...

5 Franziska Boenisch, et al. ∙

∙ 05/17/2021

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

An important problem in deep learning is the privacy and security of neu...

6 Franziska Boenisch, et al. ∙

∙ 09/25/2020

A Survey on Model Watermarking Neural Networks

Machine learning (ML) models are applied in an increasing variety of dom...

0 Franziska Boenisch, et al. ∙

∙ 02/09/2018

Tracking all members of a honey bee colony over their lifetime

Computational approaches to the analysis of collective behavior in socia...

0 Franziska Boenisch, et al. ∙

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