Privacy Considerations in Participatory Data Collection via Spatial Stackelberg Incentive Mechanisms

Jing Yang Koh, Gareth W. Peters, Ido Nevat, Derek Leong

Research output: Contribution to journalArticle

Abstract

Mobile crowd sensing is a widely used sensing paradigm allowing applications on mobile smart devices to routinely obtain spatially distributed data on a range of user attributes: location, temperature, video and audio. Such data then typically forms the input to application specific machine learning tasks to achieve objectives such as improving user experience, targeting geo-localised query based searches to user interests and commercial aspects of targeted geo-localised advertising. We consider a scenario in which the sensing application purchases data from spatially distributed smartphone users. In many spatial monitoring applications, the crowdsourcer needs to incentivize users to contribute sensing data. This may help ensure collected data has good spatial coverage, which will enhance quality of service provided to the application user when used in machine learning tasks such as spatial regression. Privacy considerations should be addressed in such crowd sensing applications, and an incentive offered to “privacy-concerned” users to contribute data. A novel Stackelberg incentive mechanism is developed that allows workers to specify their location whilst satisfying their location privacy requirements. The Stackelberg and Nash equilibria are explored and an algorithm to demonstrate the approach is developed for a real data application.

Original languageEnglish
JournalMethodology and Computing in Applied Probability
Early online date9 Jul 2020
DOIs
Publication statusE-pub ahead of print - 9 Jul 2020

Keywords

  • Incentive mechanism design
  • Location privacy
  • Mobile crowd sensing
  • Privacy
  • Stackelberg game

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)

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