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

Publication Type

Journal Article

Date Published

12/2018

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.

Journal

ACM Transactions on Sensor Networks

Volume

14

Year of Publication

2018

Short Title

ACM Trans. Sen. Netw.TOSN

Pagination

1 - 22

Issue

3-4

ISSN

15504859