Search Results for author: Daniel Bernau

Found 5 papers, 5 papers with code

Assessing Differentially Private Variational Autoencoders under Membership Inference

1 code implementation16 Apr 2022 Daniel Bernau, Jonas Robl, Florian Kerschbaum

We present an approach to quantify and compare the privacy-accuracy trade-off for differentially private Variational Autoencoders.

Time Series Time Series Analysis

Quantifying identifiability to choose and audit $ε$ in differentially private deep learning

2 code implementations4 Mar 2021 Daniel Bernau, Günther Eibl, Philip W. Grassal, Hannah Keller, Florian Kerschbaum

We transform $(\epsilon,\delta)$ to a bound on the Bayesian posterior belief of the adversary assumed by differential privacy concerning the presence of any record in the training dataset.

BIG-bench Machine Learning Inference Attack

On the privacy-utility trade-off in differentially private hierarchical text classification

1 code implementation4 Mar 2021 Dominik Wunderlich, Daniel Bernau, Francesco Aldà, Javier Parra-Arnau, Thorsten Strufe

This work investigates the privacy-utility trade-off in hierarchical text classification with differential privacy guarantees, and identifies neural network architectures that offer superior trade-offs.

General Classification Inference Attack +4

Assessing differentially private deep learning with Membership Inference

1 code implementation24 Dec 2019 Daniel Bernau, Philip-William Grassal, Jonas Robl, Florian Kerschbaum

We empirically compare local and central differential privacy mechanisms under white- and black-box membership inference to evaluate their relative privacy-accuracy trade-offs.

Inference Attack Membership Inference Attack

Reconstruction and Membership Inference Attacks against Generative Models

1 code implementation7 Jun 2019 Benjamin Hilprecht, Martin Härterich, Daniel Bernau

We present two information leakage attacks that outperform previous work on membership inference against generative models.

Density Estimation Inference Attack +1

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