基于博弈论和拍卖的数据定价综述

doi:10.11959/issn.2096-0271.2021039

Abstract

Abstract:

In the era of big data, with the explosive growth of data, regarding data as a commodity and establishing an efficient data trading market is a important thing.By data trading’s way, profit compensation is provided for data owners, and raw data or services are provided for data consumers, so that data can flow fully freely between data owners and data consumers.However, how to set a reasonable price for the data is necessary.Data pricing based on game theory and auctions was investigated.Different data pricing models under this category were investigated.These models were divided into different types, and the advantages and disadvantages of each model were compared comprehensively.Then, common data trading markets were classified, and the advantages and challenges of different data transaction frameworks in the implementation process were pointed out.A summary of existing data pricing research was made, so that scholars in the field of data pricing can more easily grasp the current research status and the key of data pricing.

Key words: data pricing, data trading market, game theory, auction

CLC Number:

TP311

Xiaowei ZHANG, Dong JIANG, Ye YUAN. A survey of game theory and auction-based data pricing[J]. Big Data Research, 2021, 7(4): 61-79.

Figures/Tables 6

参考文献	定价模型		市场结构		概述	适用场景
参考文献	定价模型	卖家	中间商	买家	概述	适用场景
[73]	VCG拍卖	一个或多个	一个	多个	服务提供商可以由卖家自身充当，也可以由多个卖家选定一个中间商充当，服务提供商根据收到的投标价格提供不同粒度的组合服务，买家根据服务提供商提供的组合，在满足质量约束和最小化社会成本的情况下，选择自己需要的组合	最小化买家的成本、“讲真话”
[10]	组合拍卖	多个	无	多个	设计一个数据市场和一个强大的实时匹配机制，有效地购买和出售机器学习任务的训练数据	公平、真实的零遗憾机制
[67]	可适用多种拍卖规则	一个或多个	一个	多个	提出了一种通用的隐私保护拍卖方案，其中拍卖商和中间平台两个独立实体组成了一个不可信的第三方交易平台。通过同态加密和一次性填充，可以确定拍卖过程中的赢家，并对所有竞价信息进行伪装，解决了CPS中的隐私保护问题	隐私保护拍卖、安全性
[70]	双边拍卖	多个	一个	多个	首先根据数据量大小对大数据分析性能的影响定义了数据成本和效用，然后提出真实、合理、计算效率高的贝叶斯利润最大化拍卖。通过求解利润最大化拍卖，得到最优服务价格和数据量，解决了服务商的利润最大化问题。服务提供商收集卖家的数据，并对卖家进行隐私补偿，同时利用自身的专业性对收集的大量数据进行处理，以满足买家的需求，为买家提供的是服务而不是原始数据	中间商收益最大化
[71]	双边拍卖	多个	无	多个	提出了一种迭代拍卖机制来协调交易，以社会福利最大化为目标。其中，卖家与买家直接发生交易，交易的是原始数据	社会福利最大化
[74]	第二价格密封拍卖+VCG拍卖	一个或多个	一个	多个	因为在多赢家拍卖策略中，传统的VCG拍卖可能会减少卖家的收益且容易受到“合谋”攻击，所以修正了第二价格密封拍卖，使在多赢家拍卖策略中能够解决上述问题。修改了多赢家拍卖策略中的优化问题，并且拍卖商可以根据标准选择最终的赢家	“讲真话”、社会福利最大化

参考文献	定价模型		市场结构		提供的服务	概述	适用场景
参考文献	定价模型	卖家	中间商	买家	提供的服务	概述	适用场景
[10]	基于拍卖的数据定价	多个	无	多个	原始数据	设计一个数据市场和一个强大的实时匹配机制，以有效地购买和出售机器学习任务的训练数据	公平、真实的零遗憾机制
[31]	Stackelberg博弈	多个	无	多个	原始数据+处理后的服务	对原始数据和提供的服务提供捆绑销售服务；使用Stackelberg博弈模型来确保服务，然后和数据消费者协商数据的价格	收益最大化
[16]	Stackelberg博弈	一个	少数	多个	原始数据	针对纯捆绑定价和分开定价两种情况，讨论了在数据销售者和市场代理商之间搭建Stackelberg博弈模型（其中数据销售者为领导者，中间商为追随者）、实现双方利益最大化的条件下，数据销售者应该采取何种定价方式使得自身利益最大化	利益最大化
[71]	基于拍卖的数据定价	多个	无	多个	原始数据	提出了一种迭代拍卖机制来协调交易，以社会福利最大化为目标，其中，数据拥有者与数据消费者直接发生交易，交易的是原始数据	社会福利最大化
[21]	基于查询的数据定价	多个	多个	一个	原始数据	传统的基于查询的数据定价集中于无套利定价方面，本模型提供了简洁的定价函数，并且实现了收益最大化	中间商收益最大化、无套利定价
[22]	基于模型的定价	一个或多个	一个或多个	一个或多个	拥有不同精度的机器学习模型	提出了一种基于模型的定价框架MBP，这个框架不销售原始数据，销售的是不同精度的机器学习模型，该框架最大的属性是无套利	无套利定价
[43]	讨价还价模型	多个	一个	多个	加入噪声后的原始数据	引入了一个差分隐私数据市场框架PRIVATA，这个框架通过讨价还价的定价机制，决定隐私ε的单位价格和单位价值，从而保证数据消费者和数据拥有者之间的地位公平	隐私保护、公平
[75]	非合作博弈	多个	多个	多个	对数据进行处理后提供服务	引入了一个数据-价格比率的参数，证明了当该参数大于1并且服务提供者有限时，市场中存在唯一有意义的纳什均衡解	充分竞争市场

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