Incentivizing Efficiency in Local Public Good Games and Applications to the Quantification of Personal Data in Networks

A well established principle arms that the privacy of individuals is respected whenever they are entitled to control the dissemination of their personal data and they are fairly compensated. From this perspective, quantifying the value of personal data is a crucial task in the Internet economics. This task is dicult, however, as the privacy attitude of the users is often characterized by a contradictory behavior, known as the privacy paradox, in which they declare to be sensitive to privacy losses but also often release large amounts of data to enjoy free services. In this paper, we model this trade-off as a local public good game and propose some quantifications of the users' personal data depending on their position in the social network, based on enhancing an efficient solution of the local public good model. In a first part, we present some non-cooperative approaches, based on an internalization of the local externalities. In a second part, we extend the model to a cooperative game and we apply some well-known solutions from cooperative game theory to suggest fair ways to compensate the users and to perform a network analysis.