A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Unsupervised Risk for Privacy
2021
2021 IEEE International Conference on Big Data (Big Data)
This position paper deals with privacy for deep neural networks, more precisely with robustness to membership inference attacks. The current state-of-the-art methods, such as the ones based on differential privacy and training loss regularization, mainly propose approaches that try to improve the compromise between privacy guarantees and decrease in model accuracy. We propose a new research direction that challenges this view, and that is based on novel approximations of the training objective
doi:10.1109/bigdata52589.2021.9671539
fatcat:bkokfhevnjarvjk5ykybbambbq