Collaborative Location Privacy with Rational Users
Recent smartphones incorporate embedded GPS devices that enable users to obtain geographic information about their surroundings by providing a location-based service (LBS) with their current coordinates. However, LBS providers collect a significant amount of data from mobile users and could be tempted to misuse it, by compromising a customer's location privacy (her ability to control the information about her past and present location). Many solutions to mitigate this privacy threat focus on changing both the architecture of location-based systems and the business models of LBS providers. MobiCrowd does not introduce changes to the existing business practices of LBS providers, rather it requires mobile devices to communicate wirelessly in a peer-to-peer fashion. To lessen the privacy loss, users seeking geographic information try to obtain this data by querying neighboring nodes, instead of connecting to the LBS. However, such a solution will only function if users are willing to share regional data obtained from the LBS provider. We model this collaborative location-data sharing problem with rational agents following threshold strategies. Initially, we study agent cooperation by using pure game theory and then by combining game theory with an epidemic model that is enhanced to support threshold strategies to address a complex multi-agent scenario. From our game-theoretic analysis, we derive cooperative and non-cooperative Nash equilibria and the optimal threshold that maximizes agents' expected utility.