UAI 2021poster68 citations
Measuring data leakage in machine-learning models with Fisher information
Awni Hannun, Chuan Guo, Laurens van der Maaten
Abstract
Machine-learning models contain information about the data they were trained on. This information leaks either through the model itself or through predictions made by the model. Consequently, when the training data contains sensitive attributes, assessing the amount of information leakage is paramount. We propose a method to quantify this leakage using the Fisher information of the model about the data. Unlike the worst-case
BibTeX
@InProceedings{pmlr-v161-hannun21a,
title = {Measuring data leakage in machine-learning models with Fisher information},
author = {Hannun, Awni and Guo, Chuan and van der Maaten, Laurens},
booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence},
pages = {760--770},
year = {2021},
editor = {de Campos, Cassio and Maathuis, Marloes H.},
volume = {161},
series = {Proceedings of Machine Learning Research},
month = {27--30 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v161/hannun21a/hannun21a.pdf},
url = {https://proceedings.mlr.press/v161/hannun21a.html},
abstract = {Machine-learning models contain information about the data they were trained on. This information leaks either through the model itself or through predictions made by the model. Consequently, when the training data contains sensitive attributes, assessing the amount of information leakage is paramount. We propose a method to quantify this leakage using the Fisher information of the model about the data. Unlike the worst-case