基于多目标效用优化的分布式数据交易算法

doi:10.11959/j.issn.1000-436x.2021034

通信学报 ›› 2021, Vol. 42 ›› Issue (2): 52-63.doi: 10.11959/j.issn.1000-436x.2021034

基于多目标效用优化的分布式数据交易算法

黄小红¹, 张勇¹, 闪德胜², 钱叶魁³, 韩璐¹, 李丹丹¹, 丛群⁴

¹ 北京邮电大学计算机学院（国家示范性软件学院），北京 100876
² 中国人民解放军32147部队，陕西宝鸡 721000
³ 陆军炮兵防空兵学院郑州校区，河南郑州 450052
⁴ 北京网瑞达科技有限公司技术研发部，北京 100876

修回日期:2020-11-22 出版日期:2021-02-25 发布日期:2021-02-01
作者简介:黄小红（1979- ），女，广州佛山人，博士，北京邮电大学教授，主要研究方向为计算机网络应用、下一代互联网和网络安全等。
张勇（1990- ），男，河北衡水人，北京邮电大学博士生，主要研究方向为区块链、大数据交易和数据隐私保护等。
闪德胜（1963- ），男，河南孟县人，中国人民解放军32147部队高级工程师，主要研究方向为网络测量、网络安全等。
钱叶魁（1980- ），男，安徽枞阳人，博士，陆军炮兵防空兵学院教授、硕士生导师，主要研究方向为网络测量、网络安全等。
韩璐（1991- ），女，蒙古族，内蒙古赤峰人，北京邮电大学博士生，主要研究方向为安全多方计算、联邦学习等。
李丹丹（1987- ），女，河南平顶山人，博士，北京邮电大学讲师，主要研究方向为网络安全、密码学等。
丛群（1980- ），男，辽宁盘锦人，北京网瑞达科技有限公司高级工程师，主要研究方向为网络管理。
基金资助:
国家重点研发计划基金资助项目(2020YFE0200500);北京邮电大学优秀博士生创新基金资助项目(CX2019212)

Distributed data trading algorithm based on multi-objective utility optimization

Xiaohong HUANG¹, Yong ZHANG¹, Desheng SHAN², Yekui QIAN³, Lu HAN¹, Dandan LI¹, Qun CONG⁴

¹ School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
² The PLA Army of 32147, Baoji 721000, China
³ Zhengzhou Campus, PLA Army Academy of Artillery and Air Defense, Zhengzhou 450052, China
⁴ Beijing WRD Technology Co., Ltd., Beijing 100876, China

Revised:2020-11-22 Online:2021-02-25 Published:2021-02-01
Supported by:
The National Key Research and Development Program of China(2020YFE0200500);The BUPT Excellent Ph.D.Students Foundation(CX2019212)

摘要/Abstract

摘要：

传统的集中式数据交易模式不能很好地适用于当前万物互联、数据实时产生的智能时代，为了使产生的数据发挥更大的价值，设计一种有效的数据交易框架至关重要。为此，提出了一种基于联盟区块链的分布式数据交易框架，在不依赖第三方的情况下实现了P2P的数据交易。针对已有数据交易模型仅考虑数据本身的因素，而忽略用户任务相关因素的问题，基于数据质量、数据属性、属性的相关性、消费者竞争等多维因素构建了双层多目标优化模型，以优化数据提供者（DP）和数据消费者（DC）的效用。为求解上述模型，提出了一种改进的多目标遗传算法——协作式NSGAII，通过DP、DC以及数据聚合器（AG）的协作进行计算。仿真结果表明，所提算法在DP和DC的效用方面取得了更好的性能，实现了更有效的数据交易。

关键词: 联盟区块链, 分布式数据交易, 优化匹配模型, 多目标遗传算法

Abstract:

The traditional centralized data trading models are not well applicable to the current intelligent era where everything is interconnected and real-time data is generated, and in order to maximize the use of collected data, it is essential to design an effective data trading framework.Therefore, a distributed data trading framework based on consortium blockchain was proposed, which realized P2P data trading without relying on a third party.Aiming at the problem that existing data trading models only consider the factors of the data itself and ignore the factors related to user tasks, a bi-level multi-objective optimization model was constructed based on multi-dimensional factors, such as data quality, data attributes, attribute relevance and consumer competition, to optimize the utilities of data provider (DP) and data consumer (DC).To solve the above model, an improved multi-objective genetic algorithm-collaborative NSGAII was proposed, calculated by the cooperation of DP, DC and data aggregator (AG).The simulation results show that the collaborative NSGAII achieves better performance in terms of the utilities of DP and DC, thus realizing more effective data trading.

Key words: consortium blockchain, distributed data trading, optimization matching model, multi-objective genetic algo-rithm

中图分类号:

TP399

黄小红, 张勇, 闪德胜, 钱叶魁, 韩璐, 李丹丹, 丛群. 基于多目标效用优化的分布式数据交易算法[J]. 通信学报, 2021, 42(2): 52-63.

Xiaohong HUANG, Yong ZHANG, Desheng SHAN, Yekui QIAN, Lu HAN, Dandan LI, Qun CONG. Distributed data trading algorithm based on multi-objective utility optimization[J]. Journal on Communications, 2021, 42(2): 52-63.

图/表 19

图1

图2

表1

参数及其含义"

参数	含义
A	AG的集合
P	a_i中DP的集合
C	a_i中DC的集合
$p_{j}$	P中的第 j位DP
c_k	C 中的第k位DC
$M_{j}^{p}$	p _j数据的属性集合
$M_{k}^{c}$	c _k 的数据属性需求集合
${PC}_{j, k}$	p _j为c_k 提供的数据产品
$M_{j, k}$	PC _j,k的属性集合
$N_{j}$	p _j的数据量
$n_{k}^{\min}$	c _k 想要购买完全满足其属性需求的最小数据量
$n_{k}^{\max}$	c _k 想要购买完全满足其属性需求的最大数据量
β	数据质量评估的抽样比例
r_j	由a_i给出的 p_j数据的质量评估
$q_{j, k}$	PC _j,k对c_k 的可用性
$x_{j, k}$	c _k 从 p_j处购买数据的数量
$w_{j, k}$	c _k 从 p_j处购买数据的单价

表1

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

表2

DP的具体参数"

DP	$\| M_{j}^{p} \|$	N _j /单元	数据噪声等级
DP₁	10	17	Ⅳ
DP₂	17	35	Ⅰ
DP₃	15	30	Ⅱ
DP₄	12	30	Ⅲ
DP₅	14	30	Ⅱ
DP₆	10	20	Ⅳ

表2

表3

DC的具体参数"

DC	$\| M_{k}^{c} \|$	$n_{k}^{\min}$ /单元	λ_k	q_{j ,k}是否熟悉
DC₁	13	27	0.85	是
DC₂	10	25	0.90	是
DC₃	15	30	0.55	否
DC₄	12	27	0.82	是
DC₅	15	29	0.62	否
DC₆	16	26	0.73	否

表3

图13

图14

图15

图16

参考文献 28

[1]	DAI W , DAIA C , CHOO K K R ,et al. SDTE:a secure blockchain-based data trading ecosystem[J]. IEEE Transactions on Information Forensics and Security, 2019,15(99): 725-737.
[2]	付钰, 俞艺涵, 吴晓平 . 大数据环境下差分隐私保护技术及应用[J]. 通信学报, 2019,40(10): 157-168.
	FU Y , YU Y H , WU X P . Differential privacy protection technology and its application in big data environment[J]. Journal on Communications, 2019,40(10): 157-168.
[3]	SPOERRY C , . A marketplace approach to trade-related risk data[C]// 2019 IEEE World Congress on Services. Piscataway:IEEE Press, 2019: 230-233.
[4]	MCKELVIE S J , MURPHY E E S , DICKSON M J ,et al. Islands of data:U.S.Patent Application 12/492,283[J].(2010-12-30)[202008-07].
[5]	KONG W , QIAO F , WU Q . Real-manufacturing-oriented big data analysis and data value evaluation with domain knowledge[J]. Computational Statistics, 2019(2): 1-24.
[6]	LU Y , HUANG X , DAI Y ,et al. Blockchain and federated learning for privacy-preserved data sharing in industrial IoT[J]. IEEE Transactions on Industrial Informatics, 2019,16(6): 4177-4186.
[7]	NIU C , ZHENG Z , WU F ,et al. Trading data in good faith:integrating truthfulness and privacy preservation in data markets[C]// 2017 IEEE 33rd International Conference on Data Engineering. Piscataway:IEEE Press, 2017: 223-226.
[8]	TIAN L , LI J , LI W ,et al. Optimal contract-based mechanisms for online data trading markets[J]. IEEE Internet of Things Journal, 2019,6(5): 7800-7810.
[9]	郭艺, 叶剑, 张鹏 . 基于偏差约减的大数据交易模型分析与修复方法[J]. 电子学报, 2018,46(7): 1754-1761.
	GUO Y , YE J , ZHANG P . Analysis and repair of big data transaction model based on deviation reduction[J]. Acta Electronica Sinica, 2018,46(7): 1754-1761.
[10]	NIYATO D , ALSHEIKH M A , WANG P ,et al. Market model and optimal pricing scheme of big data and Internet of things (IoT)[C]// 2016 IEEE International Conference on Communications (ICC). Piscataway:IEEE Press, 2016: 1-6.
[11]	CAO X , 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.
[12]	JIAO Y , WANG P , NIYATO D ,et al. Profit maximization auction and data management in big data markets[C]// 2017 IEEE Wireless Communications and Networking Conference. Piscataway:IEEE Press, 2017: 1-6.
[13]	YU H , ZHANG M . Data pricing strategy based on data quality[J]. Computers ＆ Industrial Engineering, 2017,112: 1-10.
[14]	GAO W , YU W , LIANG F ,et al. Privacy-preserving auction for big data trading using homomorphic encryption[J]. IEEE Transactions on Network Science and Engineering, 2020,7(2): 776-791.
[15]	NIU C , ZHENG Z , WU F ,et al. Achieving data truthfulness and privacy preservation in data markets[J]. IEEE Transactions on Knowledge and Data Engineering, 2018,31(1): 105-119.
[16]	JUNG T , LI X Y , HUANG W ,et al. Account Trade:accountability against dishonest big data buyers and sellers[J]. IEEE Transactions on Information Forensics and Security, 2018,14(1): 223-234.
[17]	CHEN C , WU J , LIN H ,et al. A secure and efficient blockchain-based data trading approach for Internet of vehicles[J]. IEEE Transactions on Vehicular Technology, 2019,68(9): 9110-9121.
[18]	WEBER T , PRINZ W . Trading user data:a blockchain based approach[C]// 2019 Sixth International Conference on Internet of Things:Systems,Management and Security. Piscataway:IEEE Press, 2019: 547-554.
[19]	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.
[20]	MISSIER P , BAJOUDAH S , CAPOSSELE A ,et al. Mind my value:a decentralized infrastructure for fair and trusted iot data trading[C]// Proceedings of the Seventh International Conference on the Internet of Things. New York:ACM Press, 2017: 1-8.
[21]	SABOUNCHI M , WEI J , ROCHE R . Blockchain-enabled peer-to-peer data trading mechanism[C]// 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber,Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). Piscataway:IEEE Press, 2018,doi:10.1109/Cybermatics_2018.2018.00241.
[22]	GAO J , WU T , LI X . Secure,fair and instant data trading scheme based on bitcoin[J]. Journal of Information Security and Applications, 2020,53: 102511.
[23]	HUANG X , ZHANG Y , LI D ,et al. An optimal scheduling algorithm for hybrid EV charging scenario using consortium blockchains[J]. Future Generation Computer Systems, 2019,91: 555-562.
[24]	VUKOLI? M , . The quest for scalable blockchain fabric:proof-of-work vs.BFT replication[C]// International Workshop on Open Problems in Network Security. Berlin:Springer, 2016: 112-125.
[25]	LI X J , YAO J G , LIU X ,et al. A first look at information entropy-based data pricing[C]// 2017 IEEE 37th International Conference on Distributed Computing Systems. Piscataway:IEEE Press, 2017: 2053-2060.
[26]	HANSEN P , JAUMARD B , SAVARD G . New branch-and-bound rules for linear bilevel programming[J]. SIAM Journal on scientific and Statistical Computing, 1992,13(5): 1194-1217.
[27]	BISWAS A , HOYLE C . A literature review:solving constrained non-linear bi-level optimization problems with classical methods[C]// 45th Design Automation Conference (DAC),IDETC,ASME.[S.n.:s.l.], 2019: 1-12.
[28]	ZHANG S , GUO B , DONG A ,et al. Cautionary tales on air-quality improvement in Beijing[J]. Proceedings of the Royal Society A:Mathematical,Physical and Engineering Sciences, 2017,473(2205): 20170457.

基于多目标效用优化的分布式数据交易算法

Distributed data trading algorithm based on multi-objective utility optimization

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 19

参考文献 28

相关文章 8

Metrics

推荐阅读 0

[1]	邹晓红, 许成伟, 陈晶, 宋彪, 王明月. 大规模时序图中种子节点挖掘算法研究[J]. 通信学报, 2022, 43(9): 157-168.
[2]	张勇, 李丹丹, 韩璐, 黄小红. 隐私保护的群体感知数据交易算法[J]. 通信学报, 2022, 43(5): 1-13.
[3]	刘晨曦, 王东, 陈慧玲, 李仁发. 多源异构数据融合的城市私家车流量预测研究[J]. 通信学报, 2021, 42(3): 54-64.
[4]	赵海涛,张唐伟,陈跃,赵厚麟,朱洪波. 基于DQN的车载边缘网络任务分发卸载算法[J]. 通信学报, 2020, 41(10): 172-178.
[5]	陈晶,祁子怡. 基于时序关系的社交网络影响最大化算法研究[J]. 通信学报, 2020, 41(10): 211-221.
[6]	刘丽珏,罗舒宁,高琰,陈美妃. 基于回溯蚁群-粒子群混合算法的多点路径规划[J]. 通信学报, 2019, 40(2): 102-110.
[7]	蒋仲安,王明,陈雅. 基于地理坐标和轨迹数据的路径推荐方法[J]. 通信学报, 2017, 38(5): 165-171.
[8]	迟荣华,程媛,朱素霞,黄少滨,陈德运. 基于快速高斯变换的不确定数据聚类算法[J]. 通信学报, 2017, 38(3): 101-111.