基于博弈论和拍卖的数据定价综述

doi:10.11959/issn.2096-0271.2021039

大数据 ›› 2021, Vol. 7 ›› Issue (4): 61-79.doi: 10.11959/issn.2096-0271.2021039

• 专题：新基建环境下的数据资产价值评估与定价 • 上一篇下一篇

基于博弈论和拍卖的数据定价综述

张小伟¹, 江东¹, 袁野²

¹ 东北大学计算机科学与工程学院，辽宁沈阳 110819
² 北京理工大学计算机学院，北京 100081

出版日期:2021-07-15 发布日期:2021-07-01
作者简介:张小伟（1996-），男，东北大学计算机科学与工程学院硕士生，主要研究方向为数据定价
江东（1996-），男，东北大学计算机科学与工程学院博士生，主要研究方向为GPU加速图计算、数据定价
袁野（1981-），男，北京理工大学计算机学院教授、博士生导师，主要研究方向为大数据管理与分析（包括图数据管理、众包数据管理、不确定数据管理、数据隐私保护等）。优秀青年科学基金获得者。曾获中国电子学会科学技术奖（自然科学）一等奖、全国百篇优秀博士学位论文提名奖、中国计算机学会优秀博士学位论文奖、辽宁省优秀博士学位论文奖。中国计算机学会数据库专业委员会委员、大数据专家委员会委员，中国计算机学会高级会员，IEEE、ACM高级会员。在ACM SIGMOD、VLDB、ICDE、KDD、SIGIR、IJICAI、VLDB Journal等重要学术会议和期刊上发表论文90余篇，其中CCFA类40余篇。先后承担和参加国家自然科学基金重点项目和优秀青年科学基金项目、国家重点研发计划、863计划、973计划等多项重点项目。常年担任多个国际数据库会议（共同/出版/宣传/本地）主席/程序委员会委员，包括SIGMOD、VLDB、ICDE等CCF A类会议
基金资助:
国家自然科学基金资助项目(60933001);国家自然科学基金资助项目(61932004);国家自然科学基金资助项目(62002054);国家自然科学基金资助项目(61732003);国家自然科学基金资助项目(61729201);中央高校基本科研基金资助项目(N181605012)

A survey of game theory and auction-based data pricing

Xiaowei ZHANG¹, Dong JIANG¹, Ye YUAN²

¹ School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
² School of Computer Science ＆Technology, Beijing Institute of Technology, Beijing 100081, China

Online:2021-07-15 Published:2021-07-01
Supported by:
The National Natural Science Foundation of China(60933001);The National Natural Science Foundation of China(61932004);The National Natural Science Foundation of China(62002054);The National Natural Science Foundation of China(61732003);The National Natural Science Foundation of China(61729201);The Fundamental Research Funds for the Central Universities(N181605012)

摘要/Abstract

摘要：

在大数据时代，随着数据爆炸式的增长，将数据视为一种商品，建立一个高效的数据交易市场，通过数据交易市场为数据拥有者提供利益补偿，为数据需求者提供数据或服务，使得数据能够在数据拥有者和数据需求者之间充分地自由流动显得尤为重要。然而如何为数据设定合理的价格是必须考虑的。对基于博弈论和拍卖的数据定价进行了研究，调查了该分类下不同的数据定价模型，并将其分为不同的类型，综合比较各个模型的优劣。将常见的数据交易市场进行分类，指出不同的数据交易框架在实现过程中的优点和挑战。对已有的数据定价研究进行总结，以便数据定价领域的学者能更轻松地掌握该领域的研究现状及重点。

关键词: 数据定价, 数据交易市场, 博弈论, 拍卖

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

中图分类号:

TP311

张小伟, 江东, 袁野. 基于博弈论和拍卖的数据定价综述[J]. 大数据, 2021, 7(4): 61-79.

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.

图/表 6

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图2

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图4

表1

不同拍卖机制在数据定价中的应用"

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

表1

表2

集中式市场中各种定价模型的应用"

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

表2

参考文献 81

[1]	LIN J , YU W , ZHANG N ,et al. A survey on Internet of things:architecture,enabling technologies,security and privacy,and applications[J]. IEEE Internet of Things Journal, 2017,4(5): 1125-1142.
[2]	SUN Y C , SONG H B , JARA A J ,et al. Internet of things and big data analytics for smart and connected communities[J]. IEEE Access, 2016,4: 766-773.
[3]	STANKOVIC J A . Research directions for the Internet of things[J]. IEEE Internet of Things Journal, 2014,1(1): 3-9.
[4]	WU J J , ZHAO W . Design and realization of WInternet:from net of things to Internet of things[J]. ACM Transactions on Cyber-Physical Systems, 2016,1(1): 1-12.
[5]	YANG X Y , REN X B , LIN J ,et al. On binary decomposition based privacypreserving aggregation schemes in real-time monitoring systems[J]. IEEE Transactions on Parallel and Distributed Systems, 2016,27(10): 2967-2983.
[6]	OLIVEIRA A C , FETZER C , MARTIN A ,et al. Optimizing query prices for dataas-a-service[C]// Proceedings of the 2015 IEEE International Congress on Big Data. Piscataway:IEEE Press, 2015: 289-296.
[7]	LIANG F , YU W , AN D ,et al. A survey on big data market:pricing,trading and protection[J]. IEEE Access, 2018,6: 15132-15154.
[8]	GOLDFARB A , TUCKER C . Digital economics[J]. Journal of Economic Literature, 2019,57(1): 3-43.
[9]	CHAWLA S , DEEP S , KOUTRISW P ,et al. Revenue maximization for query pricing[J]. Proceedings of the VLDB Endowment, 2019,13(1): 1-14.
[10]	AGARWAL A , DAHLEH M , SARKAR T . A marketplace for data:an algorithmic solution[C]// Proceedings of the 2019 ACM Conference on Economics and Computation. New York:ACM Press, 2019: 701-726.
[11]	MUSCHALLE A , STAHL F , L?SER A ,, et al . Pricing approaches for data markets[C]// Proceedings of the 2012 International Workshop on Business Intelligence for the Real-Time Enterprise. Heidelberg:Springer, 2012: 129-144.
[12]	KANTERE V , DASH D , GRATSIAS G ,et al. Predicting cost amortization for query services[C]// Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data. New York:ACM Press, 2011: 325-336.
[13]	NIYATO D , ALSHEIKH M A , WANG P ,et al. Market model and optimal pricing scheme of big data and Internet of things (IoT)[C]// Proceedings of the 2016 IEEE International Conference on Communications. Piscataway:IEEE Press, 2016: 1-6.
[14]	LUONG N C , HOANG D T , WANG P ,et al. Data collection and wireless communication in Internet of things (IoT) using economic analysis and pricing models:a survey[J]. IEEE Communications Surveys ＆ Tutorials, 2016,18(4): 2546-2590.
[15]	LYU X Y , ZHANG R T , YUE J . Competition and cooperation between participants of the Internet of things industry value chain[J]. Advances in Information Sciences ＆ Service Sciences, 2012,4(11): 406-412.
[16]	MEI L J , LI W , NIE K . Pricing decision analysis for information services of the Internet of things based on Stackelberg game[C]// Proceedings of the 2nd International Conference on Logistics,Informatics and Service Science. Heidelberg:Springer, 2013: 1097-1104.
[17]	JUNG K , PARK S . Privacy bargaining with fairness:privacy-price negotiation system for applying differential privacy in data market environments[C]// Proceedings of the 2019 IEEE International Conference on Big Data. Piscataway:IEEE Press, 2019: 1389-1394.
[18]	JANG B , PARK S , LEE J ,et al. Three hierarchical levels of big-data market model over multiple data sources for Internet of things[J]. IEEE Access, 2018,6: 31269-31280.
[19]	KOUTRIS P , UPADHYAYA P , BALAZINSKA M ,et al. Query-based data pricing[J]. Journal of the ACM, 2015,62(5): 1-44.
[20]	LIN B R , KIFER D . On arbitrage-free pricing for general data queries[J]. Proceedings of the VLDB Endowment, 2014,7(9): 757-768.
[21]	CHAWLA S , DEEP S ,, KOUTRIS P , et a l . Revenue maximization for query pricing[J]. arXiv preprint,2019,arXiv:1909.00845.
[22]	CHEN L , KOUTRIS P , KUMAR A . Towards model-based pricing for machine learning in a data marketplace[C]// Proceedings of the 2019 International Conference on Management of Data. New York:ACM Press, 2019: 1535-1552.
[23]	CHEN L , KOUTRIS P , KUMAR A . Modelbased pricing:do not pay for more than what you learn![C]// Proceedings of the 1st Workshop on Data Management for End-to-End Machine Learning. New York:ACM Press, 2017: 1-4.
[24]	PEI J . A survey on data pricing:from economics to data science[J]. IEEE Transactions on Knowledge and Data Engineering, 2020.
[25]	GOLDBERG A V , HARTLINE J D . Envyfree auctions for digital goods[C]// Proceedings of the 4th ACM Conference on Electronic Commerce. New York:ACM Press, 2003: 29-35.
[26]	CRETTEZ B . On Hobbes’s state of nature and game theory[J]. Theory and Decision, 2017,83(4): 499-511.
[27]	WU Q , WANG F , ZHOU L ,et al. Method of multiple attribute group decision making based on 2-dimension interval type-2 fuzzy aggregation operators with multigranularity linguistic information[J]. International Journal of Fuzzy Systems, 2017,19(6): 1880-1903.
[28]	SOLMEYER N , DIXON R , BALU R . Characterizing the Nash equilibria of a three-player Bayesian quantum game[J]. Quantum Information Processing, 2017,16(6): 146.
[29]	张维迎 . 博弈论与信息经济学[M]. 上海: 上海人民出版社, 2012.
	ZHANG W Y . Game theory and information economics[M]. Shanghai: Shanghai People’s Publishing House, 2012.
[30]	NASH J F . Non-cooperative games[M]. Princeton: Princeton University Press, 2016.
[31]	NIYATO D , HOANG D T , LUONG N C ,et al. Smart data pricing models for the Internet of things:a bundling strategy approach[J]. IEEE Network, 2016,30(2): 18-25.
[32]	ZHENG Z Z , PENG Y Q , WU F ,et al. An online pricing mechanism for mobile crowdsensing data markets[C]// Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing. New York:ACM Press, 2017: 1-10.
[33]	LI C , LI D Y , MIKLAU G ,et al. A theory of pricing private data[J]. ACM Transactions on Database Systems, 2014,39(4): 1-28.
[34]	LI Z N , YANG Z Y , XIE S L . Computing resource trading for edge-cloud-assisted Internet of things[J]. IEEE Transactions on Industrial Informatics, 2019,15(6): 3661-3669.
[35]	SIMAAN M , CRUZ J B . On the Stackelberg strategy in nonzero-sum games[J]. Journal of Optimization Theory and Applications, 1973,11(5): 533-555.
[36]	HADDADI S , GHASEMI A . Pricingbased Stackelberg game for spectrum trading in self-organised heterogeneous networks[J]. IET Communications, 2016,10(11): 1374-1383.
[37]	LIU K , QIU X Y , CHEN W H ,et al. Optimal pricing mechanism for data market in blockchain-enhanced Internet of things[J]. IEEE Internet of Things Journal, 2019,6(6): 9748-9761.
[38]	XU C Z , ZHU K , YI C Y ,et al. Data pricing for blockchain-based car sharing:a Stackelberg game approach[C]// Proceedings of the 2020 IEEE Global Communications Conference. Piscataway:IEEE Press, 2020: 1-5.
[39]	KANG X , ZHANG R , MOTANI M . Pricebased resource allocation for spectrumsharing femtocell networks:a Stackelberg game approach[J]. IEEE Journal on Selected Areas in Communications, 2012,30(3): 538-549.
[40]	YAO H , MAI T , WANG J ,et al. Resource trading in blockchain-based industrial Internet of things[J]. IEEE Transactions on Industrial Informatics, 2019,15(6): 3602-3609.
[41]	RAWAT D B , SHETTY S , XIN C . Stackelberg-game-based dynamic spectrum access in heterogeneous wireless systems[J]. IEEE Systems Journal, 2014,10(4): 1494-1504.
[42]	ZHANG T , ZHU Q . On the differential private data market:endogenous evolution,dynamic pricing,and incentive compatibility[J]. arXiv preprint,2021,arXiv:2101.04357.
[43]	JUNG K , LEE J , PARK K ,et al. PRIVATA:differentially private data market framework using negotiation-based pricing mechanism[C]// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York:ACM Press, 2019: 2897-2900.
[44]	NIU C , ZHENG Z , WU F ,et al. Unlocking the value of privacy:trading aggregate statistics over private correlated data[C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining. New York:ACM Press, 2018: 2031-2040.
[45]	YAO L , JIA Y , ZHANG H ,et al. A decentralized private data transaction pricing and quality control method[C]// Proceedings of the 2019 IEEE International Conference on Communications. Piscataway:IEEE Press, 2019: 1-5.
[46]	MOULIK S , MISRA S , GAURAV A . Costeffective mapping between wireless body area networks and cloud service providers based on multi-stage bargaining[J]. IEEE Transactions on Mobile Computing, 2016,16(6): 1573-1586.
[47]	MAO Y , CHENG T , ZHAO H ,et al. A strategic bargaining game for a spectrum sharing scheme in cognitive radio-based heterogeneous wireless sensor networks[J]. Sensors, 2017,17(12): 2737.
[48]	AZIMI S M , MANSHAEI M H , HENDESSI F . Cooperative primary-secondary dynamic spectrum leasing game via decentralized bargaining[J]. Wireless Networks, 2016,22(3): 755-764.
[49]	MCAFEE R P . A dominant strategy double auction[J]. Journal of Economic Theory, 1992,56(2): 434-450.
[50]	李三希, 王泰茗 . 拍卖理论研究述评[J]. 中国科学基金, 2021,35(1): 2-3.
	LI S X , WANG T M . A review of auction theory[J]. Bulletin of National Natural Science Foundation of China, 2021,35(1): 2-3.
[51]	许永国 . 拍卖经济理论综述[J]. 经济研究, 2002(9): 84-91,95.
	XU Y G . An overview of auction theory[J]. Economic Research Journal, 2002(9): 84-91,95.
[52]	AN D , YANG Q , YU W ,et al. Sto2Auc:a stochastic optimal bidding strategy for microgrids[J]. IEEE Internet of Things Journal, 2017,4(6): 2260-2274.
[53]	IOSIFIDIS G , GAO L , HUANG J ,et al. An iterative double auction for mobile data offloading[C]// Proceedings of the 2013 11th International Symposium and Workshops on Modeling and Optimization in Mobile,Ad Hoc and Wireless Networks. Piscataway:IEEE Press, 2013: 154-161.
[54]	AN D , YANG Q , YU W ,et al. SODA:strategyproof online double auction scheme for multimicrogrids bidding[J]. IEEE Transactions on Systems,Man,and Cybernetics:Systems, 2017,48(7): 1177-1190.
[55]	DAS S R , SUNDARAM R K . Auction theory:a summary with applications to treasury markets1[Z]. 1996.
[56]	AN D , YANG Q Y , YU W ,et al. Towards truthful auction for big data trading[C]// Proceedings of 2017 IEEE 36th International Performance Computing and Communications Conference. Piscataway:IEEE Press, 2017: 1-7.
[57]	AUSUBEL L M , MILGROM P . The lovely but lonely Vickrey auction[J]. Combinatorial Auctions, 2006,17: 22-26.
[58]	LUCIER B , PAES LEME R , TARDOS é , . On revenue in the generalized second price auction[C]// Proceedings of the 21st International Conference on World Wide Web. New York:ACM Press, 2012: 361-370.
[59]	BLUMROSEN L , NISAN N . Combinatorial auctions[J]. Algorithmic Game Theory, 2007,267:300.
[60]	SUZUKI K , KOBAYASHI K , MORITA H . Efficient sealed-bid auction using hash chain[C]// Proceedings of the International Conference on Information Security and Cryptology. Heidelberg:Springer, 2000: 183-191.
[61]	GHOSH A , ROTH A . Selling privacy at auction[C]// Proceedings of the 12th ACM Conference on Electronic Commerce. New York:ACM Press, 2011: 199-208.
[62]	FLEISCHER L K , LYU Y H . Approximately optimal auctions for selling privacy when costs are correlated with data[C]// Proceedings of the 13th ACM Conference on Electronic Commerce. New York:ACM Press, 2012: 568-585.
[63]	DANDEKAR P , FAWAZ N , IOANNIDIS S . Privacy auctions for recommender systems[J]. ACM Transactions on Economics and Computation, 2014,2(3): 1-22.
[64]	WANG Q , YE B , TANG B ,et al. Robust large-scale spectrum auctions against false-name bids[J]. IEEE Transactions on Mobile Computing, 2016,16(6): 1730-1743.
[65]	NADENDLA V S S , BRAHMA S K , VARSHNEY P K . Optimal spectrum auction design with 2-D truthful revelations under uncertain spectrum availability[J]. IEEE/ACM Transactions on Networking, 2016,25(1): 420-433.
[66]	FENG Z N , ZHU Y M , ZHANG Q ,et al. TRAC:truthful auction for locationaware collaborative sensing in mobile crowdsourcing[C]// Proceedings of the 2014 IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2014: 1231-1239.
[67]	GAO W C , YU W , LIANG F ,et al. Privacy-preserving auction for big data trading using homomorphic encryption[J]. IEEE Transactions on Network Science and Engineering, 2018,7(2): 776-791.
[68]	YANG J . The efficiency of an artificial double auction stock market with neural learning agents[M]// Evolutionary computation in economics and finance. Heidelberg: Springer, 2002: 85-105.
[69]	LI D , YANG Q , YU W ,et al. Towards double auction for assisting electric vehicles demand response in smart grid[C]// Proceedings of the 2017 13th IEEE Conference on Automation Science and Engineering. Piscataway:IEEE Press, 2017: 1604-1609.
[70]	JIAO Y T , WANG P , NIYATO D ,et al. Profit maximization auction and data management in big data markets[C]// Proceedings of the 2017 IEEE Wireless Communications and Networking Conference. Piscataway:IEEE Press, 2017: 1-6.
[71]	CAO X Y , CHEN Y , LIU K J R . Data trading with multiple owners,collectors,and users:an iterative auction mechanism[J]. IEEE Transactions on Signal and Information Processing over Networks, 2017,3(2): 268-281.
[72]	CAI H , ZHU Y M , LI J ,et al. Double auction for a data trading market with preferences and conflicts of interest[J]. The Computer Journal, 2019,62(10): 1490-1504.
[73]	WU Q , ZHOU M C , ZHU Q ,et al. VCG auction-based dynamic pricing for multigranularity service composition[J]. IEEE Transactions on Automation Science and Engineering, 2017,15(2): 796-805.
[74]	SOFIA D S , EDWARD A S . Auction based game theory in cognitive radio networks for dynamic spectrum allocation[J]. Computers ＆ Electrical Engineering, 2020,86:106734.
[75]	GUIJARRO L , PLA V , VIDAL J R ,et al. Competition in data-based service provision:Nash equilibrium characterization[J]. Future Generation Computer Systems, 2019,96: 35-50.
[76]	CHUANG I H , HUANG S H , CHAO W C ,et al. TIDES:a trust-aware IoT data economic system with blockchain-enabled multi-access edge computing[J]. IEEE Access, 2020,8: 85839-85855.
[77]	NAKAMOTO S . Bitcoin:a peer-to-peer electronic cash system[Z]. 2008.
[78]	DELGADO-SEGURA S , PéREZ-SOLà C , NAVARRO-ARRIBAS G , et al . A fair protocol for data trading based on Bitcoin transactions[J]. Future Generation Computer Systems, 2020,107: 832-840.
[79]	YU Y , DING Y J , ZHAO Y Q ,et al. LRCoin:leakage-resilient cryptocurrency based on Bitcoin for data trading in IoT[J]. IEEE Internet of Things Journal, 2018,6(3): 4702-4710.
[80]	ZHOU J Y , TANG F Y , ZHU H ,et al. Distributed data vending on blockchain[C]// Proceedings of the 2018 IEEE International Conference on Internet of Things and IEEE Green Computing and Communications and IEEE Cyber,Physical and Social Computing and IEEE Smart Data. Piscataway:IEEE Press, 2018: 1100-1107.
[81]	汪靖伟, 郑臻哲, 吴帆 ,等. 基于区块链的数据市场[J]. 大数据, 2020,6(3): 21-35.
	WANG J W , ZHENG Z Z , WU F ,et al. Blockchain based data marketplace[J]. Big Data Research, 2020,6(3): 21-35.

基于博弈论和拍卖的数据定价综述

A survey of game theory and auction-based data pricing

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