Trust model based on user types and privacy protection for personalized cloud services

doi:10.11772/j.issn.1001-9081.2014.04.0994

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (4): 994-998.DOI: 10.11772/j.issn.1001-9081.2014.04.0994

Previous Articles Next Articles

Trust model based on user types and privacy protection for personalized cloud services

LIU Fei¹,²,³,LUO Yonglong²,³,GUO Liangmin²,³,MA Yuan²,³

1.
2. Engineering Technology Research Center of Network and Information Security, Anhui Normal University, Wuhu Anhui 241003, China
3. School of Mathematics and Computer Science, Anhui Normal University, Wuhu Anhui 241003, China

Received:2013-10-17 Revised:2013-12-12 Online:2014-04-29 Published:2014-04-01
Contact: LUO Yonglong
Supported by:
National Natural Science Foundation

面向个性化云服务基于用户类型和隐私保护的信任模型

刘飞¹,²,³,罗永龙²,³,郭良敏²,³,马苑²,³

1.
2. 安徽师范大学数学计算机科学学院,安徽芜湖 241003;
3. 安徽师范大学网络与信息安全工程技术研究中心,安徽芜湖 241003

通讯作者: 罗永龙
作者简介:刘飞(1987-),男,安徽安庆人,硕士研究生,主要研究方向:信息安全;
罗永龙(1972-),男,安徽安庆人,教授,博士,主要研究方向:信息安全;
郭良敏(1980-),女,安徽合肥人,副教授,博士,主要研究方向:对等网络、网格;
马苑(1988-),男,安徽合肥人,硕士研究生,主要研究方向:信息安全。
基金资助:
国家自然科学基金资助项目;安徽省自然科学基金资助项目

Abstract

Abstract:

Concerning the problem that it is difficult for the users in cloud computing to obtain the high-quality and personalized cloud services provided by a large number of cloud providers, a trust model based on user types and privacy protection for the personalized cloud services was proposed. Firstly, the users were divided into familiar users, strange users and normal users according to the transaction history. Secondly, a fair and reasonable trust evaluation Agent was introduced to protect users' privacy, which could evaluate the trust relationship between requesters and providers based on the user types. Lastly, in view of the dynamics of trust, a new updating mechanism combined with the transaction time and transaction amount was provided based on Quality of Service (QoS). The simulation results show that the proposed model has higher transaction success rate than AARep and PeerTrust. The transaction success rate can be increased by 10% and 16% in the harsh environment where the malicious user ratio reaches 70%. This method can improve transaction success rate, and has a strong ability to withstand harsh environments.

摘要：

针对云用户难以获得个性化、高质量服务的问题，提出一种面向个性化云服务基于用户类型和隐私保护的信任模型。该模型先根据节点间的历史交易，将用户节点分为亲情节点、陌生节点及普通节点三种类型；其次，为了保护节点反馈的隐私信息，引入信任评估代理作为信任评估的主体，并且设计了基于用户类型的信任值评估方法；最后，鉴于信任的动态性，结合交易时间和交易额度提出一种新的基于服务质量的信任更新机制。实验结果表明，与AARep模型及PeerTrust模型相比，该模型不仅在恶意节点比例较低的场景中具有优势，而且在恶意节点比例超过70%的恶劣场景中，其交互成功率也分别提高了10%和16%，克服了云环境下用户节点和服务节点交互成功率低的缺点，具有较强的抵抗恶意行为的能力。

CLC Number:

TP309

LIU Fei LUO Yonglong GUO Liangmin MA Yuan. Trust model based on user types and privacy protection for personalized cloud services[J]. Journal of Computer Applications, 2014, 34(4): 994-998.

刘飞罗永龙郭良敏马苑. 面向个性化云服务基于用户类型和隐私保护的信任模型[J]. 计算机应用, 2014, 34(4): 994-998.

References

［1］TAI S. Cloud service engineering ［C］// WETICE 2009: Proceedings of the 2009 18th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises. Washington, DC: IEEE Computer Society, 2009: 3-4.
［2］FENG D, ZHANG M, ZHANG Y. Research on cloud security ［J］. Journal of Software, 2011, 22(1): 71-83. (冯登国,张敏,张妍.云计算安全研究［J］.软件学报,2011,22(1):71-83.)
［3］XIONG L, LIU L. Peertrust: supporting reputation-based trust for peer-to-peer electronic communities ［J］. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(7): 843-857.
［4］DOU W, WANG H, JIA Y, et al.A recommendation-based peer-to-peer trust model ［J］. Journal of Software, 2004, 15(4): 571-583. (窦文,王怀民,贾焰,等.构造基于推荐的peer-to-peer环境下的trust模型［J］.软件学报,2004,15(4):571-583.)
［5］KAMVAR S D, SEHLOSSERM T, GARCIA-MOLINA H. TheEigentrust algorithm for reputation management in P2P networks ［C］// WWW'03: Proceedings of the 12th International Conference on World Wide Web. New York: ACM, 2003: 640-651.
［6］LI J, JING Y, XIAO X, et al.A trust model based on similarity-weighted recommendation for P2P environments ［J］. Journal of Software, 2007, 18(1): 157-167. (李景涛,荆一楠,肖晓春,等.基于相似度加权推荐的P2P环境下的信任模型［J］.软件学报,2007,18(1):157-167.)
［7］WANG M, TAO F, ZHANG Y, et al.Accurate and adaptive reputation mechanism for P2P file sharing network ［J］. Journal of Software, 2011, 22(10): 2346-2357. (王淼,陶飞,张玉军,等.一种P2P文件共享网络高精度自适应声誉机制［J］.软件学报,2011,22(10):2346-2357.)
［8］HU C, LIU J, LIU J. Services selection based on trust evolution and union for cloud computing ［J］. Journal on Communications, 2011, 32(7): 71-79. (胡春华,刘济波,刘建勋.云计算环境下基于信任演化及集合的服务选择［J］.通信学报,2011,32(7):71-79.)
［9］XIE X, LIU L, ZHAO P. Trust model based on double incentive and deception detection for cloud computing ［J］. Journal of Electronics & Information Technology, 2012, 34(4): 812-817. (谢晓兰,刘亮,赵鹏.面向云计算基于双层激励和欺骗检测的信任模型［J］.电子与信息学报,2012,34(4):812-817.)
［10］GAO Y, SHEN B, KONG H. Trust model for cloud computingbased on SLA and user ratings ［J］. Computer Engineering, 2012, 38(7): 28-30. (高云璐,沈备军,孔华锋.基于SLA与用户评价的云计算信任模型［J］.计算机工程,2012,38(7):28-30.)
［11］HUANG H, WANG R. Subjective trust evaluation model based on membership cloud theory ［J］. Journal on Communications, 2008, 29(4): 13-19. (黄海生,王汝传.基于隶属云理论的主观信任评估模型研究［J］.通信学报,2008,29(4):13-19.)
［12］XIE L, ZHU Z, SUN L, et al.Research on behavior trust evaluation model of cloud services based on membership theory ［J］. Application Research of Computers, 2013, 30(4): 1051-1054. (谢立军,朱智强,孙磊,等.基于隶属度理论的云服务行为信任评估模型研究［J］.计算机应用研究,2013,30(4):1051-1054.)
［13］REN W, LEI M, YANG Y. DRT: a dynamical and robust trust model for software service in cloud computing ［J］. Journal of Chinese Computer Systems, 2013, 33(4): 679-683. (任伟,雷敏,杨榆.DRT:一种云计算中可信软件服务的通用动态演变鲁棒信任模型［J］.小型微型计算机系统,2013,33(4):679-683. )
［14］ZHANG R, WU X, ZHOU S, et al.A trust model based on behaviors risk evaluation ［J］. Chinese Journal of Computers, 2009, 32(4): 688-698. (张润莲,武小年,周胜源,等.一种基于实体行为风险评估的信任模型［J］.计算机学报,2009,32(4):688-698.)
［15］ZHANG L, WANG R, ZHANG Y. A trust evaluation model based on fuzzy set for grid environment ［J］. Acta Electronica Sinica, 2008, 36(5), 862-868. (张琳,王汝传,张永平.一种基于模糊集合的可用于网格环境的信任评估模型［J］.电子学报,2008,36(5):862-868.)

[1]	SHE Wei, MA Tianxiang, FENG Haige, TIAN Zhao, LIU Wei. Blockchain covert communication method based on contract call concealment#br# #br# [J]. Journal of Computer Applications, 0, (): 0-0.
[2]	Rui GAO, Xuebin CHEN, Zucuan ZHANG. Dynamic social network privacy publishing method for partial graph updating [J]. Journal of Computer Applications, 2024, 44(12): 3831-3838.
[3]	Yong XIANG, Yanjun LI, Dingyun HUANG, Yu CHEN, Huiqin XIE. Differential and linear characteristic analysis of full-round Shadow algorithm [J]. Journal of Computer Applications, 2024, 44(12): 3839-3843.
[4]	Zhenhao ZHAO, Shibin ZHANG, Wunan WAN, Jinquan ZHANG, zhi QIN. Delegated proof of stake consensus algorithm based on reputation value and strong blind signature algorithm [J]. Journal of Computer Applications, 2024, 44(12): 3717-3722.
[5]	Yiting WANG, Wunan WAN, Shibin ZHANG, Jinquan ZHANG, Zhi QIN. Linkable ring signature scheme based on SM9 algorithm [J]. Journal of Computer Applications, 2024, 44(12): 3709-3716.
[6]	Jing LIANG, Wunan WAN, Shibin ZHANG, Jinquan ZHANG, Zhi QIN. Traceability storage model of charity system oriented to master-slave chain [J]. Journal of Computer Applications, 2024, 44(12): 3751-3758.
[7]	Deyuan LIU, Jingquan ZHANG, Xing ZHANG, Wunan WAN, Shibin ZHANG, Zhi QIN. Cross-chain identity authentication scheme based on certificate-less signcryption [J]. Journal of Computer Applications, 2024, 44(12): 3731-3740.
[8]	Xin ZHANG, Jinquan ZHANG, Deyuan LIU, Wunan WAN, Shibin ZHANG, Zhi QIN. Cross-chain identity management scheme based on identity-based proxy re-encryption [J]. Journal of Computer Applications, 2024, 44(12): 3723-3730.
[9]	DENG Yilin, YU Fajiang. Pseudo-random number generator based on LSTM and separable self-attention mechanism#br# #br# [J]. Journal of Computer Applications, 0, (): 0-0.
[10]	. Correlation power analysis of advanced encryption standard algorithm based on uniform manifold approximation and projection [J]. Journal of Computer Applications, 0, (): 0-0.
[11]	. Dynamic searchable encryption scheme based on puncture pseudorandom function [J]. Journal of Computer Applications, 0, (): 0-0.
[12]	. fedPF: Federated learning for personalization and fairness [J]. Journal of Computer Applications, 0, (): 0-0.
[13]	. Review of research on conflict-based cache side-channel attacks and eviction sets [J]. Journal of Computer Applications, 0, (): 0-0.
[14]	. Secure and efficient frequency estimation method based on shuffled differential privacy [J]. Journal of Computer Applications, 0, (): 0-0.
[15]	. Blockchain-based model for notarization of simulation testing data in autonomous driving [J]. Journal of Computer Applications, 0, (): 0-0.

Trust model based on user types and privacy protection for personalized cloud services

面向个性化云服务基于用户类型和隐私保护的信任模型

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics