Competitive equilibrium based personal data market model

Abstract

Background/Objectives: In a digital economy, with the increasing commercial value, personal data is viewed as a commodity to be bought and sold. Data owners expect an appropriate compensation for trading off their privacy depending on how they value their privacy. Simultaneously, data consumers want to maximize their utility which is dependent on the value derived from the data. Consequently, a data market model that optimally recompenses data owners and maximizes the profits of data consumers is required. Methods: In this study, a data market model and pricing mechanism based on Fisher market model and competitive equilibrium are presented where the value derived from the data is calculated from information entropy. The proposed data model and the pricing mechanism jointly and simultaneously maximize the profit of data owners and the utility of data buyers. Findings: Experiments are conducted on adult data set to validate the efficacy of the proposed approach. Data owners are classified as risk averse, risk neutral, risk taking and privacy regarders. Subsequently, prices of data samples are adjusted to reach equilibrium as defined by the Fisher market model maximizing simultaneously and jointly the profit of data owners and the utility of data buyers. Applications: The proposed competitive equilibrium based personal data market model can be used to find equilibrium prices and bundles of data samples for each data buyer at these prices maximizing the utility of data buyer subject to his budgetary constraints and data requirements, and data owners' privacy preferences.