关键词: 数据供应链, 定价, 隐私级别, 大众健康数据价值

Abstract: The contradiction between the huge potential value of health data to improve the public health and the few available health data results in the presence of the platform for health data trading. At the same time, comprehensive health data have become an important management asset of Health Data Trading Platform (HDP). The first problem faced by the platform in the operation process is how to encourage the public to share data. After collecting health data through monetary incentives, HDP will consider how to realize the data value by providing data for data demanders such as insurance companies, pharmaceutical companies, health service organization and so on.In practice, the public pays different attention to privacy, and will share data and control the privacy levels according to the utility. And various types of data demanders need data with different privacy levels for their special purposes. Hence, HDP has to deal with how to determine the monetary return to the public who provide data according to privacy levels, and the data price sold to data demanders based on the order quantity, privacy level and matching degree. Because the pricing of health data is a new research field and most of the data trading platforms have just started, these pricing problems have not yet been systematically and scientifically answered in practical operation and academic research.
Taking the hard questions above into account, this paper first gives relevant literature on data pricing, data value mining and service pricing, and summarizes the previous research’s contribution and the challenging work at the moment which urgently needs resolution. Combined with practical commercial cases, the health service supply chain system is composed of the public, the health data trading platform and the data demanders. Meanwhile,this paper focuses on some important decision variables in the system such as monetary return, platform data selling price including fixed price and variable price and data demand quantity. Considering the data value and the privacy level of health data, the paper applies the principal-agent and incentive mechanism theory to present health data pricing and the monetization of public shared data in a supply chain with a HDP and data demanders. Based on the innovation on the data value function, the data pricing models are built for analyzing fixed and variable prices and the optimal pricing strategies are discussed in detail. We also establish the decision models of monetization of public shared data affected by privacy levels, the platform’s data processing ability, the matching degree of supply and demand data and the value increment coefficient on the decision variables.
The findings are as follows: (1)Health data trading platform can set the two types of optimal price for data according to different privacy levels. (2)The optimal decision-making of the platform’s monetary returns to the public is determined by the privacy cost rate, which is positively correlated with the matching degree of supply and demand data under certain conditions, and negatively correlated with the platform’s data processing ability. Under certain conditions, the data demand will increase with the improvement of the platform’s data processing ability and the matching degree of supply and demand data. (3)Given the data value function, data demanders have the optimal ordering quantities under two privacy cases, which are determined by the platform’s capability of data processing and matching degree. (4)Moreover, we obtain the following observations from corresponding numerical analysis. (a)The public can always get more monetary returns when sharing high privacy data compared with those sharing low privacy data. (b)The fixed price of low privacy data first decreases and then increases with the enhancement of platform data processing capability.
The achievement of this paper lies in its essential definition of the data value in the frame of a data supply chain, which is affected by every member in the system, giving the quantification methods of monetary incentives for the public’s sharing data,and expanding the consideration factors of data value function, so as to provide effective decision-making methods and strategies for the data service supply chain, and guidance for the data trading platform when making price decisions. Hopefully, data market segmentation can be further incorporated into the operations models in future work. In addition, we would like to appreciate the National Natural Science Foundation of China for its substantial support and the local government for related project funds at ministerial and provincial-level.

Key words: data supply chain, pricing, privacy level, value of public’s health data