Privacy-Preserving Data Aggregation for Mobile Crowdsensing with Externality: An Auction Approach

Mengyuan Zhang, Lei Yang, Shibo He, Ming Li, Junshan Zhang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We develop an auction framework for privacy-preserving data aggregation in mobile crowdsensing, where the platform plays the role as an auctioneer to recruit workers for sensing tasks. The workers are allowed to report noisy versions of their data for privacy protection; and the platform selects workers by taking into account their sensing capabilities to ensure the accuracy level of the aggregated result. Observe that when moving the control of data privacy from the data aggregator to the workers, the data aggregator has limited market power in the sense that it can only partially control the noise by judiciously choosing a subset of workers based on workers' privacy preferences. This introduces externalities because the privacy of each worker depends on the total noise in the aggregated result that in turn relies on which workers are selected. Specifically, we first consider a privacy-passive scenario where workers participate if their privacy loss can be adequately compensated by the rewards. We explicitly characterize the externalities and the hidden monotonicity property of the problem, making it possible to design a truthful, individually rational and computationally efficient incentive mechanism. We then extend the results to a privacy-proactive scenario where workers have individual requirements for their perceivable data privacy levels. Our proposed mechanisms for both scenarios can select a subset of workers to (nearly) minimize the cost of purchasing their private sensing data subject to the accuracy requirement of the aggregated result. We validate the proposed scheme through theoretical analysis as well as extensive simulations.

Original languageEnglish (US)
Article number9354053
Pages (from-to)1046-1059
Number of pages14
JournalIEEE/ACM Transactions on Networking
Volume29
Issue number3
DOIs
StatePublished - Jun 2021

Keywords

  • Crowd sensing
  • data aggregation
  • incentive mechanism
  • privacy-preserving

ASJC Scopus subject areas

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

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