How to Balance Privacy and Money through Pricing Mechanism in Personal Data Market

In the big data era, personal data is, recently, perceived as a new oil or currency in the digital world. Both public and private sectors wish to use such data for studies and businesses. However, access to such data is restricted due to privacy issues. Seeing the commercial opportunities in gaps between demand and supply, the notion of personal data market is introduced. While there are several challenges associated with rendering such a market operational, we focus on two main technical challenges: (1) How should personal data be fairly traded under a similar e-commerce platform? (2) How much should personal data be worth in trade? In this paper, we propose a practical personal data trading framework that strikes a balance between money and privacy. To acquire insight on user preferences, we first conduct an online survey on human attitude toward privacy and interest in personal data trading. Second, we identify five key principles of the personal data trading central to designing a reasonable trading framework and pricing mechanism. Third, we propose a reasonable trading framework for personal data, which provides an overview of how data are traded. Fourth, we propose a balanced pricing mechanism that computes the query price and perturbed results for data buyers and compensation for data owners (whose data are used) as a function of their privacy loss. Finally, we conduct an experiment on our balanced pricing mechanism, and the result shows that our balanced pricing mechanism performs significantly better than the baseline mechanism.