A Framework for Personalized Location Privacy

Ben Niu, Qinghua Li, Hanyi Wang, Guohong Cao, Fenghua Li, Hui Li

Research output: Contribution to journalArticlepeer-review

Abstract

Location privacy has been one of the most important research areas over recent years, and many location Privacy Preserving Mechanisms (PPMs) have been proposed. Each PPM typically achieves certain tradeoffs between privacy protection and resource consumption, and no PPM performs perfectly in all cases. Instead of designing one PPM that works for all cases, this paper studies how to make the best use of multiple single PPMs for location privacy protection in different scenarios. In particular, we propose a general framework called SmartGuard, which dynamically selects the best privacy preservation strategy for a user based on her preferences and the current status of her mobile device. SmartGuard quantifies user privacy under various scenarios, models the effects of different PPMs on several key factors such as the remaining battery level and network bandwidth, and then recommends the best privacy strategy for the user. To illustrate how our SmartGuard works, we apply it to a specific scenario of LBSs and implement it on Android based phones. Evaluation results show that our solution outperforms existing PPMs under various scenarios.

Original languageEnglish (US)
JournalIEEE Transactions on Mobile Computing
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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