隐私保护的群体感知数据交易算法

doi:10.11959/j.issn.1000-436x.2022082

摘要/Abstract

摘要：

为解决群体感知数据交易模式下参与者数据隐私泄露的问题，提出了一种隐私保护的群体感知数据交易算法。首先，为实现对参与者的隐私保护，设计了基于差分隐私的聚合方案，参与者不再需要上传原始数据，而是按照任务需求对收集的数据进行分析和计算，将任务结果按照平台分配的隐私预算添加噪声后发送给平台；其次，为确保参与者的可信性，构建了参与者的信誉模型；最后，为激励消费者和参与者参与交易，在考虑消费者对结果偏差的容忍约束和参与者的隐私泄露补偿的基础上构建了交易优化模型以优化平台的收益，并给出了基于遗传算法的收益优化算法（POA）来求解该模型。仿真结果表明，POA不仅保护了参与者的隐私，而且在平台的收益方面相比于VENUS和DPDT分别提高了29.27%和20.45%。

关键词: 群体感知, 数据交易, 差分隐私, 信誉模型

Abstract:

To solve the problem that data privacy leakage of participants under the crowd-sensed data trading model, a privacy-protected crowd-sensed data trading algorithm was proposed.Firstly, to achieve the privacy protection of participants, an aggregation scheme based on differential privacy was designed.Participants were no longer needed to upload raw data, but analyzed and calculated the collected data according to the task requirements, and then sent the analysis results to the platform after adding noise in accordance with the privacy budget allocated by the platform to protect their privacy.Secondly, in order to ensure the credibility of participants, a reputation model of participants was proposed.Finally, in order to encourage consumers and participants to participate in transactions, a data trading optimization model was constructed by considering the consumer’s constraint on the result deviation，the participant’s privacy leakage compensation and platform profit, and a POA based on genetic algorithm was proposed to solve the model.The simulation results show that the POA not only protects the privacy of participants, but also increases the profit of the platform by 29.27% and 20.45% compared to VENUS and DPDT, respectively.

Key words: crowd sensing, data trading, differential privacy, reputation model

中图分类号:

TP399

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.

Yong ZHANG, Dandan LI, Lu HAN, Xiaohong HUANG. Privacy-protected crowd-sensed data trading algorithm[J]. Journal on Communications, 2022, 43(5): 1-13.

图/表 16

图1

图2

表1

系统参数和变量的含义"

参数和变量	含义
P	数据交易平台
U	消费者的集合
u_j	U 中的第 j位消费者
γ_j	u _j向P提交的任务
$θ_{j}^{\max}$	u _j可容忍的最大偏差
W	参与者的集合
w_i	W中的第i位参与者
B_i	w _i 的报价
r_{i ,j}	w _i 对任务γ_j计算的结果
?_i,j	w _i 对r_i,j 所添加的噪声
r_j	P对任务γ_j聚合得到的结果
q_{i ,j}	w _i 针对任务γ_j的归一化权重
$θ_{j}^{*}$	u _j的最优偏差
$c_{j}^{*}$	u _j的报价
X	参与者的招募方案
ε_i,j	针对任务γ_j，w_i 的隐私预算
θ_j	针对任务γ_j，聚合后结果的偏差程度

表1

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

图13

图14

图15

参考文献 36

[1]	DAI W Q , DAI C K , CHOO K K R ,et al. SDTE:a secure blockchain-based data trading ecosystem[J]. IEEE Transactions on Information Forensics and Security, 2020,15: 725-737.
[2]	HUANG Y D , ZENG Y M , YE F ,et al. Fair and protected profit sharing for data trading in pervasive edge computing environments[C]// Proceedings of IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2020: 1718-1727.
[3]	WANG X D , YING C H , LUO Y . Privacy-friendly decentralized data aggregation for mobile crowdsensing[C]// Proceedings of 2020 IEEE Global Communications Conference. Piscataway:IEEE Press, 2020: 1-6.
[4]	YU J L , CHEUNG M H , HUANG J W ,et al. Mobile data trading:behavioral economics analysis and algorithm design[J]. IEEE Journal on Selected Areas in Communications, 2017,35(4): 994-1005.
[5]	YANG G , HE S B , SHI Z G ,et al. Promoting cooperation by the social incentive mechanism in mobile crowdsensing[J]. IEEE Communications Magazine, 2017,55(3): 86-92.
[6]	杜小勇, 陈峻, 陈跃国 . 大数据探索式搜索研究[J]. 通信学报, 2015,36(12): 77-88.
	DU X Y , CHEN J , CHEN Y G . Exploratory search on big data[J]. Journal on Communications, 2015,36(12): 77-88.
[7]	AN B Y , XIAO M J , LIU A ,et al. Truthful crowdsensed data trading based on reverse auction and blockchain[C]// Database Systems for Advanced Applications,Berlin:Springer, 2019: 292-309.
[8]	ZHENG Z Z , PENG Y Q , WU F ,et al. Trading data in the crowd:profit-driven data acquisition for mobile crowdsensing[J]. IEEE Journal on Selected Areas in Communications, 2017,35(2): 486-501.
[9]	JIANG C K , GAO L , DUAN L J ,et al. Scalable mobile crowdsensing via peer-to-peer data sharing[J]. IEEE Transactions on Mobile Computing, 2018,17(4): 898-912.
[10]	ZHENG Y F , DUAN H Y , YUAN X L ,et al. Privacy-aware and efficient mobile crowdsensing with truth discovery[J]. IEEE Transactions on Dependable and Secure Computing, 2020,17(1): 121-133.
[11]	ZHANG L , LI Y N , XIAO X ,et al. CrowdBuy:privacy-friendly image dataset purchasing via crowdsourcing[C]// Proceedings of IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2018: 2735-2743.
[12]	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, 2020,7(2): 776-791.
[13]	NIU C Y , ZHENG Z Z , WU F ,et al. Achieving data truthfulness and privacy preservation in data markets[J]. IEEE Transactions on Knowledge and Data Engineering, 2019,31(1): 105-119.
[14]	ZHAO Y Q , YU Y , LI Y N ,et al. Machine learning based privacy-preserving fair data trading in big data market[J]. Information Sciences, 2019,478: 449-460.
[15]	WANG J , WANG Y L , ZHAO G S ,et al. Location protection method for mobile crowd sensing based on local differential privacy preference[J]. Peer-to-Peer Networking and Applications, 2019,12(5): 1097-1109.
[16]	XUE Q , ZHU Y W , WANG J . Mean estimation over numeric data with personalized local differential privacy[J]. Frontiers of Computer Science, 2021,16(3): 1-10.
[17]	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.
[18]	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.
[19]	YU H F , ZHANG M X . Data pricing strategy based on data quality[J]. Computers ＆ Industrial Engineering, 2017,112: 1-10.
[20]	JIAO Y T , WANG P , NIYATO D ,et al. Profit maximization auction and data management in big data markets[C]// Proceedings of 2017 IEEE Wireless Communications and Networking Conference. Piscataway:IEEE Press, 2017: 1-6.
[21]	黄小红, 张勇, 闪德胜 ,等. 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.
	HUANG X H , ZHANG Y , SHAN D S ,et al. Distributed data trading algorithm based on multi-objective utility optimization[J]. Journal on Communications, 2021,42(2): 52-63.
[22]	ZHANG J X , SUN J C , ZHANG R ,et al. Privacy-preserving social media data outsourcing[C]// Proceedings of IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2018: 1106-1114.
[23]	CAI H , YE F , YANG Y Y ,et al. Towards privacy-preserving data trading for web browsing history[C]// Proceedings of the International Symposium on Quality of Service. Piscataway:IEEE Press, 2019: 1-10.
[24]	YANG L , ZHANG M Y , HE S B ,et al. Crowd-empowered privacy-preserving data aggregation for mobile crowdsensing[C]// Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing. New York:ACM Press, 2018: 151-160.
[25]	DWORK C , . Differential privacy[C]// Automata,Languages and Programming. Berlin:Springer, 2006: 1-12.
[26]	KEMP F . The Laplace distribution and generalizations:a revisit with applications to communications,economics,engineering,and finance[J]. Journal of the Royal Statistical Society:Series D (the Statistician), 2003,52(4): 698-699.
[27]	DANDEKAR P , FAWAZ N , IOANNIDIS S . Privacy auctions for recommender systems[C]// Internet and Network Economics. Berlin:Springer, 2012: 309-322.
[28]	JIN H M , SU L , XIAO H P ,et al. INCEPTION:incentivizing privacy-preserving data aggregation for mobile crowd sensing systems[C]// Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing. New York:ACM Press, 2016: 341-350.
[29]	MENG C S , JIANG W J , LI Y L ,et al. Truth discovery on crowd sensing of correlated entities[C]// Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. New York:ACM Press, 2015: 169-182.
[30]	KAZAI G , KAMPS J , MILIC-FRAYLING N , . An analysis of human factors and label accuracy in crowdsourcing relevance judgments[J]. Information Retrieval, 2013,16(2): 138-178.
[31]	STRUBELL E , GANESH A , MCCALLUM A . Energy and policy considerations for modern deep learning research[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020,34(9): 13693-13696.
[32]	WANG L Y , ZHANG D Q , YAN Z X ,et al. effSense:a novel mobile crowd-sensing framework for energy-efficient and cost-effective data uploading[J]. IEEE Transactions on Systems,Man,and Cybernetics:Systems, 2015,45(12): 1549-1563.
[33]	汪民乐 . 遗传算法的收敛性研究[J]. 计算技术与自动化, 2015,34(1): 58-62.
	WANG M L . Research on convergence of genetic algorithm[J]. Computing Technology and Automation, 2015,34(1): 58-62.
[34]	GAO G J , XIAO M J , WU J ,et al. DPDT:a differentially private crowd-sensed data trading mechanism[J]. IEEE Internet of Things Journal, 2020,7(1): 751-762.
[35]	CAI H , ZHU Y M , LI J ,et al. A profit-maximizing mechanism for query-based data trading with personalized differential privacy[J]. The Computer Journal, 2020,64(2): 264-280.
[36]	ZHENG S Y , CAO Y , YOSHIKAWA M . Trading data with personalized differential privacy and partial arbitrage freeness[J]. arXiv Preprint,arXiv:2105.01651, 2021.