Incentive mechanism for privacy-aware data aggregation in mobile crowd sensing systems

Haiming Jin, Lu Su, Houping Xiao, Klara Nahrstedt

Research output: Contribution to journalArticlepeer-review

Abstract

The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to the public crowd equipped with various mobile devices. A fundamental issue in such systems is to effectively incentivize worker participation. However, instead of being an isolated module, the incentive mechanism usually interacts with other components which may affect its performance, such as data aggregation component that aggregates workers' data and data perturbation component that protects workers' privacy. Therefore, different from the past literature, we capture such interactive effect and propose INCEPTION, a novel MCS system framework that integrates an incentive, a data aggregation, and a data perturbation mechanism. Specifically, its incentive mechanism selects workers who are more likely to provide reliable data and compensates their costs for both sensing and privacy leakage. Its data aggregation mechanism also incorporates workers' reliability to generate highly accurate aggregated results, and its data perturbation mechanism ensures satisfactory protection for workers' privacy and desirable accuracy for the final perturbed results. We validate the desirable properties of INCEPTION through theoretical analysis as well as extensive simulations.

Original languageEnglish (US)
Article number8438556
Pages (from-to)2019-2032
Number of pages14
JournalIEEE/ACM Transactions on Networking
Volume26
Issue number5
DOIs
StatePublished - Oct 2018

Keywords

  • Incentive mechanism
  • data aggregation
  • mobile crowd sensing
  • privacy preservation

ASJC Scopus subject areas

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

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