A Reasonable Data Pricing Mechanism for Personal Data Transactions with Privacy Concern

In the past few years, more and more data marketplaces for personal data transactions sprung up. However, it is still very challenging to estimate the value of privacy contained in the personal data. Especially when the buyer already has some related datasets, he is able to obtain more privacy by combining and analyzing the bought data and the data he already has. The main research motivation of this work is to reasonably price the data with privacy concern. We propose a reasonable data pricing mechanism which prices the personal privacy data from three aspects and is different from the existing work, we propose a new concept named `privacy cost' to quantitatively measure the privacy information increment after a data transaction rather than directly measuring the privacy information contained in a single dataset. In addition, we use the information entropy as an important index to measure the information content of data. And we conduct a set of experiments on our personal data pricing method, and the results show that our pricing method performs better than the alternatives.