Crowd-empowered privacy-preserving data aggregation for mobile crowdsensing

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 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 a sensing task. In this framework, the workers are allowed to report privacy-preserving versions of their data to protect their data privacy; and the platform selects workers based on their sensing capabilities, which aims to address the drawbacks of game-theoretic models that cannot ensure the accuracy level of the aggregated result, due to the existence of multiple Nash Equilibria. Observe that in this auction based framework, there exists externalities among workers' data privacy, because the data privacy of each worker depends on both her injected noise and the total noise in the aggregated result that is intimately related to which workers are selected to fulfill the task. To achieve a desirable accuracy level of the data aggregation in a cost-effective manner, we explicitly characterize the externalities, i.e., the impact of the noise added by each worker on both the data privacy and the accuracy of the aggregated result. Further, we explore the problem structure, characterize the hidden monotonicity property of the problem, and determine the critical bid of workers, which makes it possible to design a truthful, individually rational and computationally efficient incentive mechanism. The proposed incentive mechanism can recruit a set of workers to approximately minimize the cost of purchasing private sensing data from workers 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)
Title of host publicationMobihoc 2018 - Proceedings of the 2018 19th International Symposium on Mobile Ad Hoc Networking and Computing
PublisherAssociation for Computing Machinery
Pages151-160
Number of pages10
ISBN (Electronic)9781450357708
DOIs
StatePublished - Jun 26 2018
Event19th ACM International Symposium on Mobile Ad-Hoc Networking and Computing, MobiHoc 2018 - Los Angeles, United States
Duration: Jun 26 2018Jun 29 2018

Other

Other19th ACM International Symposium on Mobile Ad-Hoc Networking and Computing, MobiHoc 2018
CountryUnited States
CityLos Angeles
Period6/26/186/29/18

Keywords

  • Crowd sensing
  • Data aggregation
  • Incentive mechanism
  • Privacy-preserving

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Software

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  • Cite this

    Yang, L., Zhang, M., He, S., Li, M., & Zhang, J. (2018). Crowd-empowered privacy-preserving data aggregation for mobile crowdsensing. In Mobihoc 2018 - Proceedings of the 2018 19th International Symposium on Mobile Ad Hoc Networking and Computing (pp. 151-160). Association for Computing Machinery. https://doi.org/10.1145/3209582.3209598