Recent years have seen the explosion of online business models based on the exploitation of users’ personal data collected on social networks. The potential benefits of this ecosystem, however, are currently undermined by tussles around privacy and the uncontrolled use of this data. In this talk, we propose and analyze new game-theoretic models that allow us to discuss economics and incentives challenges arising in a personal data ecosystem where users have control over their data.
In the first part of the talk, we present a user-centric public good model, in which users have full control of their personal information and strategically choose whether they want to reveal some data and with which precision. We analyze the users’ incentives to obfuscate their data and to reveal truthfully the precision of the data disclosed. We then propose simple ways in which an analyst collecting the data can increase the aggregate precision level by slightly modifying the game.
In the second part of the talk, we focus on the economic value of personal data. In a social network, the value of data revealed depends on data revealed by other users. To analyze the impact of the social network on this value, we propose a cooperative game theoretic model on a graph. We then propose a fair mechanism based on the Shapley value to quantify the value of the personal information of each user depending on his social interactions, and we analyze the impact on the system of our mechanism.