Author name cluster

Frederik Harder

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

TMLR Journal 2023 Journal Article

Pre-trained Perceptual Features Improve Differentially Private Image Generation

Frederik Harder
Milad Jalali
Danica J. Sutherland
Mijung Park

Training even moderately-sized generative models with differentially-private stochastic gradient descent (DP-SGD) is difficult: the required level of noise for reasonable levels of privacy is simply too large. We advocate instead building off a good, relevant representation on an informative public dataset, then learning to model the private data with that representation. In particular, we minimize the maximum mean discrepancy (MMD) between private target data and a generator's distribution, using a kernel based on perceptual features learned from a public dataset. With the MMD, we can simply privatize the data-dependent term once and for all, rather than introducing noise at each step of optimization as in DP-SGD. Our algorithm allows us to generate CIFAR10-level images with $\epsilon \approx 2$ which capture distinctive features in the distribution, far surpassing the current state of the art, which mostly focuses on datasets such as MNIST and FashionMNIST at a large $\epsilon \approx 10$. Our work introduces simple yet powerful foundations for reducing the gap between private and non-private deep generative models. Our code is available at https://github.com/ParkLabML/DP-MEPF.

PDF Details

ICML Conference 2022 Conference Paper

Hermite Polynomial Features for Private Data Generation

Margarita Vinaroz
Mohammad-Amin Charusaie
Frederik Harder
Kamil Adamczewski
Mijung Park

Kernel mean embedding is a useful tool to compare probability measures. Despite its usefulness, kernel mean embedding considers infinite-dimensional features, which are challenging to handle in the context of differentially private data generation. A recent work, DP-MERF (Harder et al. , 2021), proposes to approximate the kernel mean embedding of data distribution using finite-dimensional random features, which yields an analytically tractable sensitivity of approximate kernel mean embedding. However, the required number of random features in DP-MERF is excessively high, often ten thousand to a hundred thousand, which worsens the sensitivity of the approximate kernel mean embedding. To improve the sensitivity, we propose to replace random features with Hermite polynomial features. Unlike the random features, the Hermite polynomial features are ordered, where the features at the low orders contain more information on the distribution than those at the high orders. Hence, a relatively low order of Hermite polynomial features can more accurately approximate the mean embedding of the data distribution compared to a significantly higher number of random features. As a result, the Hermite polynomial features help us to improve the privacy-accuracy trade-off compared to DP-MERF, as demonstrated on several heterogeneous tabular datasets, as well as several image benchmark datasets.

Details

AAAI Conference 2020 Conference Paper

Interpretable and Differentially Private Predictions

Frederik Harder
Matthias Bauer
Mijung Park

Interpretable predictions, which clarify why a machine learning model makes a particular decision, can compromise privacy by revealing the characteristics of individual data points. This raises the central question addressed in this paper: Can models be interpretable without compromising privacy? For complex “big” data ﬁt by correspondingly rich models, balancing privacy and explainability is particularly challenging, such that this question has remained largely unexplored. In this paper, we propose a family of simple models with the aim of approximating complex models using several locally linear maps per class to provide high classiﬁcation accuracy, as well as differentially private explanations on the classiﬁcation. We illustrate the usefulness of our approach on several image benchmark datasets as well as a medical dataset.

PDF Details