Spatial Stackelberg Incentive Mechanism for Privacy-Aware Mobile Crowd Sensing

Journal of Machine Learning Research 1 (2000) 1-48

31 Pages Posted: 9 May 2018

See all articles by Jing Yang Koh

Jing Yang Koh

National University of Singapore (NUS) - Department of Information Systems and Analytics

Gareth Peters

Department of Actuarial Mathematics and Statistics, Heriot-Watt University; University College London - Department of Statistical Science; University of Oxford - Oxford-Man Institute of Quantitative Finance; London School of Economics & Political Science (LSE) - Systemic Risk Centre; University of New South Wales (UNSW) - Faculty of Science

Ido Nevat

Heriot-Watt University - Department of Actuarial Mathematics and Statistics

Derek Leong

Agency for Science, Technology and Research (A*STAR) - Institute for Infocomm Research; Agency for Science, Technology and Research (A*STAR)

Date Written: 2000

Abstract

Mobile crowd sensing is an emerging sensing paradigm where sensing applications buy sensor data from spatially distributed mobile smartphone users (workers) instead of deploying their own sensor networks. This reduces costs and enhances the range of possible applications that can be developed, since the application developers can focus on other tasks such as spatial field reconstruction or estimation of some spatial characteristics for a process being sensed.

In many spatial monitoring applications, the crowdsourcer needs to incentivize smartphone users to contribute sensing data such that the collected dataset has good spatial coverage and can be used for accurate spatial regression that will meet quality of service requirements applicable to the product on offer from the application.

To further incentivize privacy-concerned workers to contribute, we propose a novel Stackelberg incentive mechanism that allows workers to specify their location whilst satisfying their location privacy requirements. We then derive a unique Stackelberg equilibrium which demonstrates the stability of our approach. Next, we prove the existence of a Stackelberg equilibrium in this spatial game context in which the crowdsourcer imposes constraints on the minimum and maximum data contributions for each user, and we study sufficient conditions for achieving Pareto efficiency. We develop efficient algorithms to solve for the equilibrium for the follower and leader games via a backward induction formulation.

We apply our theoretical and methodological results to a real-world application and our simulation results show that our proposed Stackelberg incentive model is better in terms of predictive mean and variance compared to the disk and k-depth coverage maximizing schemes.

Suggested Citation

Koh, Jing Yang and Peters, Gareth and Nevat, Ido and Leong, Derek, Spatial Stackelberg Incentive Mechanism for Privacy-Aware Mobile Crowd Sensing (2000). Journal of Machine Learning Research 1 (2000) 1-48 , Available at SSRN: https://ssrn.com/abstract=3158616

Jing Yang Koh

National University of Singapore (NUS) - Department of Information Systems and Analytics ( email )

Singapore

Gareth Peters (Contact Author)

Department of Actuarial Mathematics and Statistics, Heriot-Watt University ( email )

Edinburgh Campus
Edinburgh, EH14 4AS
United Kingdom

HOME PAGE: http://garethpeters78.wixsite.com/garethwpeters

University College London - Department of Statistical Science ( email )

1-19 Torrington Place
London, WC1 7HB
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

University of Oxford Eagle House
Walton Well Road
Oxford, OX2 6ED
United Kingdom

London School of Economics & Political Science (LSE) - Systemic Risk Centre ( email )

Houghton St
London
United Kingdom

University of New South Wales (UNSW) - Faculty of Science ( email )

Australia

Ido Nevat

Heriot-Watt University - Department of Actuarial Mathematics and Statistics ( email )

Edinburgh, Scotland EH14 4AS
United Kingdom

Derek Leong

Agency for Science, Technology and Research (A*STAR) - Institute for Infocomm Research ( email )

1 Fusionopolis Way
#16-16 Connexis
Singapore, 138632
Singapore

Agency for Science, Technology and Research (A*STAR) ( email )

1 Fusionopolis Way
#16-16 Connexis
Singapore, 138632
Singapore

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