Generative Adversarial User Privacy in Lossy Single-Server Information Retrieval

Chung Wei Weng, Yauhen Yakimenka, Hsuan Yin Lin, Eirik Rosnes, Jorg Kliewer

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)3495-3510
Number of pages16
JournalIEEE Transactions on Information Forensics and Security
Volume17
DOIs
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

Keywords

  • Compression
  • data-driven framework
  • generative adversarial networks
  • generative adversarial privacy
  • information-theoretical privacy
  • private information retrieval

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