吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (10): 2897-2908.doi: 10.13229/j.cnki.jdxbgxb.20211336

• 计算机科学与技术 • 上一篇    下一篇

面向SaaS隐私保护的自适应访问控制方法

范大娟1,2(),黄志球2,曹彦2   

  1. 1.南京工程学院 计算机工程学院，南京 211167
    2.南京航空航天大学 计算机科学与技术学院，南京 210016
  • 收稿日期:2021-12-06 出版日期:2023-10-01 发布日期:2023-12-13
  • 作者简介:范大娟（1982-），女，副教授，博士.研究方向：云计算与服务计算，访问控制，隐私保护.E-mail：fanbingjie523@126.com
  • 基金资助:
    国家自然科学基金项目(61802174);江苏省自然科学基金项目(BK20210928);河南省高等学校重点科研项目(22A520040);河南省科技攻关项目(222102210048);南京工程学院校级科研基金项目(CKJB201906)

Adaptive access control method for SaaS privacy protection

Da-juan FAN1,2(),Zhi-qiu HUANG2,Yan CAO2   

  1. 1.School of Computer Engineering，Nanjing Institute of Technology，Nanjing 211167，China
    2.College of Computer Science and Technology，Nanjing University of Aeronautics and Astronautics，Nanjing 210016，China
  • Received:2021-12-06 Online:2023-10-01 Published:2023-12-13

RICH HTML

 4   

PDF (PC)

215

摘要：

针对现有访问控制方法未针对云计算隐私保护特征且缺少动态隐私授权机制，无法在运行过程中自适应保护隐私数据的不足，通过对基于角色的访问控制（RBAC）模型进行信任度和隐私使用行为扩展，提出了一种面向云计算SaaS层隐私保护的访问控制模型TBRBAC。在此基础上，提出了一种SaaS服务信任度评估和动态更新机制，给出了基于TBRBAC（Trust and Behavior based RBAC）模型的自适应隐私访问控制系统的体系结构、执行流程以及授权分析算法，讨论了自适应访问控制流程的合理性，并通过实例分析和实验验证说明了本文方法的可行性和有效性。该方法能够实现运行时动态隐私授权及细粒度的隐私访问控制，增强了云计算环境下隐私数据的安全保护。

关键词: 计算机软件, 云计算, SaaS服务, 访问控制, 隐私保护, 自适应

Abstract:

Aiming at the problem that the existing access control methods do not consider the features of cloud computing privacy protection， lack dynamic privacy authorization mechanism， and thus can not protect privacy data adaptively in the running process， an access control model TBRBAC for SaaS layer privacy protection in cloud computing is proposed by extending RBAC model with the trust degree and privacy use behavior. On this basis， a trust degree evaluation and dynamic update mechanism for SaaS services is proposed. Then， the architecture of adaptive privacy access control system based on TBRBAC model， its execution process and authorization analysis algorithm are given. The rationality of the adaptive access control process is also discussed. Finally， the feasibility and effectiveness of this method are illustrated by example analysis and experimental verification. This method can achieve dynamic privacy authorization and fine-grained privacy access control at run-time， and enhance the security protection of private data in cloud computing environment.

Key words: computer software, cloud computing, SaaS service, access control, privacy protection, adaptive

中图分类号: 

  • TP311

图1

TBRBAC模型"

图2

目的树的例子"

图3

自适应隐私访问控制系统体系结构"

图4

网上商店实例的SaaS服务交互场景"

表1

用户Jack的隐私访问控制需求表"

隐私数据信任度期望目的期望保留时长
name，phone_number≥0.6｛Purchase，Contract｝2 weeks
name，credit_card_number≥0.8｛Pay｝1 hour
postal_address，E-mail≥0.3｛Delivery，T-Email｝permanent-retention

表2

SaaS服务的隐私数据请求表"

SaaS服务请求者隐私数据敏感度请求目的请求保留时长
Order Servicename，phone_number0.4｛Purchase｝1 day
Pay Servicename，credit_card_number0.5｛Credit-Card-Pay｝no-retention
Delivery Servicepostal_address，E-mail0.2｛Delivery，Marketing｝1 month

图5

隐私泄露事件危害程度对信任度的影响"

图6

衰减率对信任度的影响"

表3

授权动态性验证中的角色权限关系"

角色信任度隐私权限数量隐私数据
r1≥0.32pd1，pd2
r2≥0.52pd3，pd4
r3≥0.73pd5，pd6，pd7
r4≥0.21pd8

图7

不同时刻隐私权限数量的变化"

图8

时间消耗对比"

图9

内存占用对比"

表4

相关工作比较"

方法采用技术是否支持信任度/敏感度是否支持目的是否支持保留时长是否支持自适应
文献［14，17］可满足性检测××××
文献［15］可满足性检测×√××
文献［16］可满足性检测√√√×
文献［8，9］访问控制×√××
文献［18］访问控制√×××
文献［19，20］访问控制×××√
本文访问控制√√√√
1 Stephanie V, Chamikara M, Khalil I, et al. Privacy-preserving location data stream clustering on mobile edge computing and cloud[J]. Information Systems, 2021, 10(2): 17-28.
2 Iyengar R, Zoom Fung B., the video conferencing app everyone is using, faces questions over privacy[EB/OL]. [2020-04-01]. .
3 Sun P. Research on adaptive relationship between trust and privacy in cloud service[J]. IEEE Access, 2021, 9: 43214-43227.
4 彭焕峰, 黄志球, 范大娟, 等. 面向服务组合的用户隐私需求规约与验证方法[J]. 软件学报, 2016, 27(8): 1948-1963.
Peng Huan-feng, Huang Zhi-qiu, Fan Da-juan, et al. Specification and verification of user privacy requirements for web service composition[J]. Journal of Software, 2016, 27(8): 1948-1963.
5 Ding W, Hu R, Yan Z, et al. An extended framework of privacy-preserving computation with flexible access control[J]. IEEE Transactions on Network and Service Management, 2020, 17(2): 918-930.
6 Qun N, Bertino I E, Lobo J, et al. Privacy-aware role-based access control[J]. ACM Transactions on Information & System Security, 2010, 13(3): 1-31.
7 Wang H, Sun L L, Bertino E. Building access control policy model for privacy preserving and testing policy conflicting problems[J]. Journal of Computer & System Sciences, 2014, 80(8): 1493-1503.
8 Omoronyia I. Privacy engineering in dynamic settings[C]∥Proc of IEEE/ACM International Conference on Software Engineering Companion, Washington, USA, 2017: 297-299.
9 Sun P. Research on cloud computing service based on trust access control[J]. International Journal of Engineering Business Management, 2020, 12: 1-13.
10 余波, 台宪青, 马治杰. 云计算环境下基于属性和信任的RBAC模型研究[J]. 计算机工程与应用, 2020, 56(9): 84-92.
Yu Bo, Tai Xian-qing, Ma Zhi-jie. Study on attribute and trust-based RBAC model in cloud computing[J]. Computer Engineering and Applications, 2020, 56(9): 84-92.
11 Kolter J, Netter M, Pernul G. Visualizing past personal data disclosures[C]∥Proceedings of IEEE International Conference on Availability, Reliability, and Security, Krakow, Poland, 2010: 131-139.
12 Jiang J X, Huang Z Q, Ma W W, et al. Using information flow analysis to detect implicit information leaks for web service composition[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(4): 494-502.
13 Pitsiladis G V, Stefaneas P. Implementation of privacy calculus and its type checking in Maude[C]∥Proc of the 8th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, Limassol, Cyprus, 2018: 477-493.
14 Costante E, Paci F, Zannone N. Privacy-aware web service composition and ranking[C]∥Proc of the 20th IEEE International Conference on Web Services, Salt Lake City, USA, 2013: 131-138.
15 Ke C B, Xiao F, Huang Z Q . et al. Ontology-based privacy data chain disclosure discovery method for big data[J]. IEEE Transactions on Services Computing,2022,15(1): 59-68.
16 Accorsi R, Lehmann A, Lohmann N. Information leak detection in business process models[J]. Theory, Application, and Tool Support: Information Systems, 2015, 47: 244-257.
17 Ding H F, Peng C G, Tian Y L, et al. Privacy risk adaptive access control model via evolutionary game[J]. Journal on Communications, 2019, 40(12): 9-20.
18 Amardeep K, Amandeep V. An abstract model for adaptive access control in cloud computing[C]∥Proc of International Conference on Recent Advancement on Computer and Communication, Andhra Pradesh, India, 2018: 269-277.
[1] 张刘,曾庆铭,赵寰宇,范国伟. 基于Lyapunov理论的卫星大挠性太阳能帆板分布式自适应振动抑制控制方法[J]. 吉林大学学报(工学版), 2023, 53(9): 2676-2685.
[2] 于雅静,郭健,王荣浩,秦伟,宋明武,向峥嵘. 基于状态观测器的多四旋翼无人机时变编队控制[J]. 吉林大学学报(工学版), 2023, 53(3): 871-882.
[3] 樊敏,宋世军. 基于多源大数据分析的图像特征智能识别模型[J]. 吉林大学学报(工学版), 2023, 53(2): 555-561.
[4] 吴剑,许斌. 基于CEEMDAN理论的堆积层滑坡位移区间预测模型及仿真[J]. 吉林大学学报(工学版), 2023, 53(2): 562-568.
[5] 马敏,胡大伟,舒兰,马壮林. 城市轨道交通网络韧性评估及恢复策略[J]. 吉林大学学报(工学版), 2023, 53(2): 396-404.
[6] 白丽丽,姜封国,周玉明,曾枭. 基于改进鲸鱼算法的结构可靠性优化设计[J]. 吉林大学学报(工学版), 2023, 53(11): 3160-3165.
[7] 武狄,何鸣. 基于机器视觉的视频SAR运动目标检测方法[J]. 吉林大学学报(工学版), 2023, 53(11): 3214-3220.
[8] 陈永刚,许继业,王海涌,熊文祥. 基于自适应神经模糊网络的转辙机故障诊断方法[J]. 吉林大学学报(工学版), 2023, 53(11): 3274-3280.
[9] 杜睿山,陈雨欣,孟令东. 可信云计算平台多源大数据时间序列调度算法[J]. 吉林大学学报(工学版), 2023, 53(11): 3194-3200.
[10] 董立岩,梁伟业,王越群,李永丽. 基于会话的结合全局潜在信息的图神经网络推荐模型[J]. 吉林大学学报(工学版), 2023, 53(10): 2964-2972.
[11] 秦严严,杨晓庆,王昊. 智能网联混合交通流CO2排放影响及改善方法[J]. 吉林大学学报(工学版), 2023, 53(1): 150-158.
[12] 李永明,裴小轩,伊曙东. 混合动力汽车动力电池自适应神经网络优化控制[J]. 吉林大学学报(工学版), 2022, 52(9): 2063-2068.
[13] 张冲,胡云峰,宫洵,孙耀. 燃料电池阴极流量无模型自适应滑模控制器设计[J]. 吉林大学学报(工学版), 2022, 52(9): 2085-2095.
[14] 刘岩,丁天威,王宇鹏,都京,赵洪辉. 基于自适应控制的燃料电池发动机热管理策略[J]. 吉林大学学报(工学版), 2022, 52(9): 2168-2174.
[15] 王克勇,鲍大同,周苏. 基于数据驱动的车用燃料电池故障在线自适应诊断算法[J]. 吉林大学学报(工学版), 2022, 52(9): 2107-2118.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发