【作者】 王晨；

【导师】 贾俊秀； 于鲲鹏；

【作者基本信息】 西安电子科技大学 ， 工程硕士（专业学位）， 2022， 硕士

【摘要】 健康数据对提升公众健康水平的巨大潜在价值与可利用的健康数据很少的矛盾激发了健康数据交易平台的诞生,健康数据交易平台在运营过程中首先面临的问题是如何激励大众共享数据,通过货币激励的方式收集健康数据后,平台会考虑为数据需求方提供数据,数据需求方通过数据分析提供服务给公众,进而提升公众健康水平。现实生活中公众对隐私的重视程度不同,他们会共享不同隐私级别的数据。且存在不同类型的数据需求方,他们因使用数据的目的不同而需要不同隐私级别的数据。则平台回馈给提供不同隐私级别数据的大众的货币回报、出售给数据需求方不同隐私级别数据的价格及不同需求方提供的数据分析服务价格的合理设置是整条供应链稳定运营的基础。由于健康数据定价问题是新兴研究领域且数据交易平台大多刚起步,这些定价问题在实际运营和学术研究中尚未得到系统而科学的解答。在此背景下,首先对数据定价、数据价值挖掘和服务定价等研究现状进行梳理与分析,总结现有研究对本研究的支撑依据及还未研究的内容。结合商业实际案例确定由大众、健康数据交易平台及数据需求方构成的健康服务供应链,明确共享数据的货币回报、平台数据出售价格和健康数据分析服务价格等重要决策变量。研究平台回馈给提供不同隐私级别数据的大众的货币回报、出售给需求方不同隐私级别数据的固定价格和可变价格、不同数据需求方的数据需求量及服务定价问题。利用委托代理及激励机制理论构建以激励大众共享健康数据及不同类型数据需求方购买自身真正需要数据为目标的数据定价及数据分析服务定价模型。并探讨了平台处理数据能力、供需数据匹配程度和价值增值系数对决策变量的影响。研究发现:(1)健康数据交易平台可为不同隐私级别的数据制定最优价格。(2)平台回馈给大众的货币回报最优决策的获得由隐私成本率决定,并且货币回报在一定条件下与供需数据匹配程度呈正相关,与平台处理数据能力呈负相关。数据需求量在一定条件下随平台处理数据能力的提升及供需数据匹配程度的增加而增加。(3)数据需求方提供的数据分析服务价格和数据需求量随平台出售数据的可变价格的增加而减少。(4)数据分析服务的价格随价值增值系数的增加而增加。数值分析表明:(1)大众共享高隐私级别数据总能比共享低隐私级别数据获得更多的货币回报。(2)低隐私数据固定价格随平台数据处理能力的增强先减小后增大。(3)低隐私数据需求方使用低隐私级别数据提供的数据分析服务价格随供需数据匹配程度的增加而增加。研究成果填补了健康数据定价中大众因共享数据而获得回报的量化这一理论空白,拓展了数据价值函数的考虑因素,为健康服务供应链定价提供了有效的决策依据,为数据交易平台在决策价格时提供了指导意见。更多还原

【Abstract】 The contradiction between the huge potential value of health data to improve the public health level and the few available health data inspired the birth of the health data trading platform.The first problem faced by the health data trading platform in the operation process is how to encourage the public to share data.After collecting health data through monetary incentives,the platform will consider providing data for data demanders,and the data demanders will provide health data analysis services to the public through data analysis,thus improving the public health level.In real life,the public pays different attention to privacy,and they will share data of different privacy levels.And there are different types of data demanders,who need data with different privacy levels for different purposes.Then the monetary return of the platform to the public who provide data with different privacy levels,the price of data with different privacy levels sold to data demanders and the reasonable setting of data analysis service prices provided by different demanders are the basis for the stable operation of the whole supply chain.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 been systematically and scientifically answered in practical operation and academic research.Under this background,this paper first sorts out and analyzes the research status of data pricing,data value mining and service pricing,and summarizes the supporting basis of existing research for this study and the contents that have not been studied yet.Combined with practical commercial cases,the health service supply chain system composed of the public,health data trading platform and data demanders is determined,and important decision variables such as monetary return of shared data,platform data selling price and health data analysis service price are defined.This paper studies the monetary returns of the platform to the public who provide data with different privacy levels,the fixed and variable prices of data sold to demanders,and the data demand and service pricing of different data demanders.Based on the principal-agent theory and incentive mechanism theory,the data pricing model and data analysis service pricing model are constructed to encourage the public to share health data and different types of data demanders to buy the data they really need.The influence of the platform’s data processing ability,the matching degree of supply and demand data and the value increment coefficient on the decision variables is also discussed.The findings are as follows:(1)Health data trading platform can set the optimal price for data with different privacy levels.(2)The acquisition of the optimal decision-making of the platform’s monetary return to the public is determined by the privacy cost rate,and the monetary return 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 increase of the matching degree of supply and demand data.(3)The data analysis service price and data demand provided by the data demander decrease with the increase of the variable price of data sold by the platform.(4)The price of data analysis services increases with the increase of value-added coefficient.Numerical analysis shows that:(1)The public can always get more monetary returns when sharing high privacy data than when sharing low privacy data.(2)The fixed price of low privacy data first decreases and then increases with the enhancement of platform data processing capability.(3)The price of data analysis service provided by low privacy data demanders using low privacy level increases with the increase of matching degree of supply and demand data.The research results of this paper fill up the theoretical gap in the pricing of health data,that is,the quantification of the public’s reward for sharing data,expand the consideration factors of data value function,provide effective decision-making and basis for the pricing of health service supply chain,and provide guidance for the data trading platform when making price decisions.更多还原