Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11009-020-09798-7
Title: Privacy Considerations in Participatory Data Collection via Spatial Stackelberg Incentive Mechanisms
Authors: Koh, Jing Yang
Peters, Gareth W.
Nevat, Ido
Leong, Derek
Keywords: Incentive mechanism design
Location privacy
Mobile crowd sensing
Privacy
Stackelberg game
Issue Date: 9-Jul-2020
Publisher: Springer
Citation: Koh, Jing Yang, Peters, Gareth W., Nevat, Ido, Leong, Derek (2020-07-09). Privacy Considerations in Participatory Data Collection via Spatial Stackelberg Incentive Mechanisms. Methodology and Computing in Applied Probability 23 (3) : 1097-1128. ScholarBank@NUS Repository. https://doi.org/10.1007/s11009-020-09798-7
Rights: Attribution 4.0 International
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. © 2020, The Author(s).
Source Title: Methodology and Computing in Applied Probability
URI: https://scholarbank.nus.edu.sg/handle/10635/233504
ISSN: 1387-5841
DOI: 10.1007/s11009-020-09798-7
Rights: Attribution 4.0 International
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