Privacy Considerations in Participatory Data Collection via Spatial Stackelberg Incentive Mechanisms

Jing Yang Koh, Gareth W. Peters (), Ido Nevat and Derek Leong
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Jing Yang Koh: National University of Singapore
Gareth W. Peters: Department of Actuarial Mathematics and Statistics, Heriot-Watt
Ido Nevat: TUMCREATE Ltd, 1 CREATE Way, CREATE Tower
Derek Leong: Institute for Infocomm Research (I2R), Agency for Science, Technology Research (A*STAR)

Methodology and Computing in Applied Probability, 2021, vol. 23, issue 3, 1097-1128

Abstract: 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.

Keywords: Incentive mechanism design; Stackelberg game; Location privacy; Mobile crowd sensing; Privacy; 91 Game theory; economics; finance and ... 91Bxx; 91Axx; 62 Statistics; 62Cxx; 68 Computer Science; 68Wxx (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11009-020-09798-7

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