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

Abstract

© 2018 Association for Computing Machinery. 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.

Authors:
 [1];  [2];  [3];  [2];  [1]
  1. University of Southern Denmark, Denmark
  2. University of California, Berkeley, California
  3. Lawrence Berkeley National Laboratory, Berkeley, California
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind Energy Technologies Office (EE-4WE)
OSTI Identifier:
1526547
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
ACM Transactions on Sensor Networks
Additional Journal Information:
Journal Volume: 14; Journal Issue: 3-4; Journal ID: ISSN 1550-4859
Publisher:
Association for Computing Machinery
Country of Publication:
United States
Language:
English

Citation Formats

Sangogboye, Fisayo Caleb, Jia, Ruoxi, Hong, Tianzhen, Spanos, Costas, and Kjærgaard, Mikkel Baun. A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems. United States: N. p., 2018. Web. doi:10.1145/3275520.
Sangogboye, Fisayo Caleb, Jia, Ruoxi, Hong, Tianzhen, Spanos, Costas, & Kjærgaard, Mikkel Baun. A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems. United States. doi:10.1145/3275520.
Sangogboye, Fisayo Caleb, Jia, Ruoxi, Hong, Tianzhen, Spanos, Costas, and Kjærgaard, Mikkel Baun. Tue . "A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems". United States. doi:10.1145/3275520.
@article{osti_1526547,
title = {A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems},
author = {Sangogboye, Fisayo Caleb and Jia, Ruoxi and Hong, Tianzhen and Spanos, Costas and Kjærgaard, Mikkel Baun},
abstractNote = {© 2018 Association for Computing Machinery. 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.},
doi = {10.1145/3275520},
journal = {ACM Transactions on Sensor Networks},
number = 3-4,
volume = 14,
place = {United States},
year = {2018},
month = {11}
}

Journal Article:
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This content will become publicly available on November 27, 2019
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