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A First Look at Information Entropy-Based Data Pricing
Xijun Li, Jianguo Yao, Xue Liu and Haibing Guan
Shanghai Jiao Tong University, Shanghai Jiao Tong University, McGill University, Shanghai Jiao Tong University

Distribution of intangible information goods is experiencing tremendous growth in recent years, which has facilitated a blossoming of information goods economics. As big data develops, there are more and more information goods markets for data trading. In the current of data pricing policies in data trading, there are many metrics to measure the value of data goods, such as the data generation date, data volume, and data integrity, etc. However, it is very challenging to identify the amount of data information and its distribution, and the corresponding data pricing has rarely been discussed. In this paper, we propose a new data pricing metric, i.e., the data information entropy, which helps to make a reasonable price in the data trading. We first demonstrate a data information measurement method based on information entropy, and then propose a pricing function based on the result of data information measurement. To comprehensively understand the new data pricing metric and facilitate its application in data trading, we verify the rationality of the data information measurement method and give three concrete pricing functions. It is the first time to look at the information entropy-based data pricing, which can inspire the research concerning the pricing mechanism of data goods, further promoting the development of data products business.