Application-driven privacy-preserving data publishing with correlated attributes

Aria Rezaei, Chaowei Xiao, Jie Gao, Bo Li, Sirajum Munir

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Recent advances in computing have allowed for the possibility to collect large amounts of data on personal activities and private living spaces. To address the privacy concerns of users in this environment, we propose a novel framework called PR-GAN that offers privacy-preserving mechanism using generative adversarial networks. Given a target application, PR-GAN automatically modifies the data to hide sensitive attributes – which may be hidden and can be inferred by machine learning algorithms – while preserving the data utility in the target application. Unlike prior works, the pub-lic’s possible knowledge of the correlation between the target application and sensitive attributes is built into our modeling. We formulate our problem as an optimization problem, show that an optimal solution exists and use generative adversarial networks (GAN) to create perturbations. We further show that our method provides privacy guarantees under the Pufferfish framework, an elegant generalization of the differential privacy that allows for the modeling of prior knowledge on data and correlations. Through experiments, we show that our method outperforms conventional methods in effectively hiding the sensitive attributes while guaranteeing high performance in the target application, for both property inference and training purposes. Finally, we demonstrate through further experiments that once our model learns a privacy-preserving task, such as hiding subjects’ identity, on a group of individuals, it can perform the same task on a separate group with minimal performance drops.

Original languageEnglish (US)
Title of host publicationInternational Conference on Embedded Wireless Systems and Networks, EWSN 2021
EditorsPolly Huang, Marco Zuniga
PublisherJunction Publishing
ISBN (Print)9780994988652
StatePublished - 2021
Externally publishedYes
EventInternational Conference on Embedded Wireless Systems and Networks, EWSN 2021 - Delft, Netherlands
Duration: Feb 17 2021Feb 19 2021

Publication series

NameInternational Conference on Embedded Wireless Systems and Networks
ISSN (Electronic)2562-2331


ConferenceInternational Conference on Embedded Wireless Systems and Networks, EWSN 2021

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Electrical and Electronic Engineering


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