A personalized privacy protection data uploading scheme for mobile crowdsensing

Rong Ma, Lei Chen, Jinbo Xiong, Youliang Tian, Qi Li, Ximeng Liu

Research output: Contribution to book or proceedingConference articlepeer-review

2 Scopus citations

Abstract

Mobile crowdsensing is a typical application of the Industrial Internet of hings and brings a promising opportunity to construct powerful industrial systems and services. The existing privacy protection schemes usually adopt a unified privacy protection policy for all sensing data due to lack of effective privacy metrics, thereby resulting in excessive or insufficient protection of privacy information and low accuracy of sensing data. To tackle these issues, in this paper, we propose a personalized privacy protection data uploading (PPDU) scheme based on game theory and sensing user's historical spatio-temporal data. In PPDU, a personalized privacy measurement algorithm is designed to obtain the personalized privacy degree of user sensing data. Based on the privacy degree, a user uploading behavior game is designed by employing an incomplete information game, and a game-based spatio-temporal sensing data uploading method is proposed for user privacy protection. Simulation experiments with real trajectory data show that the PPDU scheme makes a reasonable balance between retaining high quality of crowdsensing service and protecting user's privacy.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Industrial Internet Cloud, ICII 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages227-232
Number of pages6
ISBN (Electronic)9781728129778
DOIs
StatePublished - Nov 2019
Event2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019 - Orlando, United States
Duration: Nov 10 2019Nov 12 2019

Publication series

NameProceedings - IEEE International Conference on Industrial Internet Cloud, ICII 2019

Conference

Conference2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019
Country/TerritoryUnited States
CityOrlando
Period11/10/1911/12/19

Keywords

  • Game theory
  • Industrial Internet of Things
  • Mobile crowdsensing
  • Personalized privacy protection
  • Privacy degree

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