Toward monetizing personal data: A two-sided market analysis
2019 Elsevier B.V. With the increasing popularity of social mobile applications and mobile crowd sensing, holders of smart devices are generating a huge amount of personal data. Nowadays, a wide variety of domains ranging from health-care applications to pollution monitoring are benefiting from collected data. In fact, these personal data may have a monetary value and currently, secondary data owners (such as clinics, Facebook and Twitter) are getting benefit from them either by reselling these data to third entities or by generating statistical analysis. Unfortunately, the primary data owners, the users themselves, are not getting benefit from these transactions. Today, there is no platform to help users monetize their own personal data. In this paper, we propose a two-sided market-based platform for monetizing personal data. Given the intrinsic properties of data as economic good, we prove formally that two-sided market is a realistic solution as it can offer the service of collecting the required data amount and within the quality range required by the buyers. More precisely, (1) we study the two-sided platform equilibrium under non-linear externalities and extract mathematically the condition that states which side will be subsidized by the platform; (2) we study formally the impact of the direct sale mechanism on the platform payoff and show that the platform payoff is given by a logarithmic function of end users stability in the platform; and finally (3) using a real dataset from Amazon, we construct an empirical comparison between the two-sided platform model and the classic merchant model. In addition, we simulate the efficiency of the two-sided market model in presence of direct sale. Simulation results show that our two-sided market platform can play a critical role in motivating users to share their personal data and can be a practical solution for monetizing data generated from mobile crowd sensing.