Game theoretic approach to location sharing with privacy in a community-based mobile safety application

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Game theoretic approach to location sharing with privacy in a community-based mobile safety application

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International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems archive
Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems table of contents

Vancouver, British Columbia, Canada

SESSION: Availability, connectivity, and localization table of contents

Pages 229-238

Year of Publication: 2008

ISBN:978-1-60558-235-1

Authors

Hua Liu	University of Southern California, Los Angeles, CA, USA
Bhaskar Krishnamachari	University of Southern California, Los Angeles, CA, USA
Murali Annavaram	University of Southern California, Los Angeles, CA, USA

Sponsors

ACM: Association for Computing Machinery
SIGSIM: ACM Special Interest Group on Simulation and Modeling

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ACM New York, NY, USA

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ABSTRACT

A new generation of community-based social networking mobile applications is emerging. In these applications, there is often a fundamental tension between users' desire for preserving the privacy of their own data and their need for fine-grained information about others. Our work is motivated by a community-based mobile application called Aegis, a personal safety enhancement service based on sharing location information with trusted nearby friends. We model the privacy-participation tradeoffs in this application using a game theoretic formulation. Users in this game are assumed to be self-interested. They prefer to obtain more fine-grained knowledge from others while limiting their own privacy leak (i.e. their own contributions to the game) as much as possible. We design a tit-for-tat mechanism to give user incentives to contribute to the application. We investigate the convergence of two best response dynamics to achieve a non-trivial Nash equilibrium for this game. Further, we propose an algorithm that yields a Pareto optimal Nash equilibrium. We show that this algorithm guarantees polynomial time convergence and can be executed in a distributed manner.

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