A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems

A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems

TitleA Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems
Publication TypeJournal Article
Year of Publication2018
AuthorsFisayo Caleb Sangogboye, Ruoxi Jia, Tianzhen Hong, Costas Spanos, Mikkel Baun Kjærgaard
JournalACM Transactions on Sensor Networks
Volume14
Issue3-4
Pagination1 - 22
Date Published12/2018
ISSN15504859
Keywordscyber physical systems, deep learning, k-anonymity, Privacy preservation, Smart buildings
Abstract

Cyber-physical systems have enabled the collection of massive amounts of data in an unprecedented level of spatial and temporal granularity. Publishing these data can prosper big data research, which, in turn, helps improve overall system efficiency and resiliency. The main challenge in data publishing is to ensure the usefulness of published data while providing necessary privacy protection. In our previous work (Jia et al. 2017a), we presented a privacy-preserving data publishing framework (referred to as PAD hereinafter), which can guarantee k-anonymity while achieving better data utility than traditional anonymization techniques. PAD learns the information of interest to data users or features from their interactions with the data publishing system and then customizes data publishing processes to the intended use of data. However, our previous work is only applicable to the case where the desired features are linear in the original data record. In this article, we extend PAD to nonlinear features. Our experiments demonstrate that for various data-driven applications, PAD can achieve enhanced utility while remaining highly resilient to privacy threats.

DOI10.1145/329407010.1145/3275520
Short TitleACM Trans. Sen. Netw.TOSN