"Generative Adversarial User Privacy in Lossy Single-Server Information" by Chung Wei Weng, Yauhen Yakimenka et al.
 

Generative Adversarial User Privacy in Lossy Single-Server Information Retrieval

Document Type

Article

Publication Date

1-1-2022

Abstract

We propose to extend the concept of private information retrieval by allowing for distortion in the retrieval process and relaxing the perfect privacy requirement at the same time. In particular, we study the trade-off between download rate, distortion, and user privacy leakage, and show that in the limit of large file sizes this trade-off can be captured via a novel information-theoretical formulation for datasets with a known distribution. Moreover, for scenarios where the statistics of the dataset is unknown, we propose a new deep learning framework by leveraging a generative adversarial network approach, which allows the user to learn efficient schemes from the data itself. We evaluate the performance of the scheme on a synthetic Gaussian dataset as well as on the MNIST, CIFAR-10, and LSUN datasets. For the MNIST, CIFAR-10, and LSUN datasets, the data-driven approach significantly outperforms a nonlearning-based scheme which combines source coding with the download of multiple files.

Identifier

85137927594 (Scopus)

Publication Title

IEEE Transactions on Information Forensics and Security

External Full Text Location

https://doi.org/10.1109/TIFS.2022.3203320

e-ISSN

15566021

ISSN

15566013

First Page

3495

Last Page

3510

Volume

17

This document is currently not available here.

Share

COinS