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基于本体推理的隐私保护访问控制机制研究 被引量:2

Research on Privacy Protection Access Control Mechanism Based on Ontology Reasoning
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摘要 通过访问控制的方法保护用户隐私被研究者们采纳,访问控制通过验证访问者的身份的合法性来限制非法用户对数据的访问,从而有效避免隐私泄露。但目前该方法存在不考虑用户隐私信息、访问控制粒度较粗等问题,不能满足所有隐私主体的需求,也不能最大限度地保护用户的隐私信息。文章提出一种基于本体推理的隐私信息保护访问控制机制,该机制能够从所有隐私主体的隐私信息角度出发,用本体推理的方法从访问控制粒度方面进行信息优化,从隐私主体的角度考虑更多隐私主体的隐私需求。实验结果表明文章提出的访问控制机制能够更好地保护用户隐私。 Access control restricts illegal users’access to data by verifying the legitimacy of the identity of visitors,thus effectively avoiding privacy leakage.However,the method does not consider the user privacy information,the granularity of access control is relatively coarse,can not meet the needs of all privacy subjects,and can not protect the user privacy information to the maximum extent.This article aims to put forward a kind of reasoning based on ontology to protect privacy information access control mechanism,this mechanism can be from the perspective of all privacy subject's privacy information,the use of ontology reasoning method used for access control,the information was optimized from the aspects of access control granularity,and from the angle of the subject of privacy and considering the privacy requirement more privacy subject.Experimental analysis shows that the access control mechanism proposed in this paper can better protect user privacy.
作者 靳姝婷 何泾沙 朱娜斐 潘世佳 JIN Shuting;HE Jingsha;ZHU Nafei;PAN Shijia(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)
出处 《信息网络安全》 CSCD 北大核心 2021年第8期52-61,共10页 Netinfo Security
基金 国家重点研发计划[2019QY(Y)0601]。
关键词 本体推理 隐私保护 访问控制 语义分析 ontology reasoning privacy protection access control semantic analysis
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  • 1姚键,茅兵,谢立.一种基于有向图模型的安全策略冲突检测方法[J].计算机研究与发展,2005,42(7):1108-1114. 被引量:29
  • 2王小明,赵宗涛.基于角色的时态对象存取控制模型[J].电子学报,2005,33(9):1634-1638. 被引量:18
  • 3袁禄来,曾国荪,王伟.基于Dempster-Shafer证据理论的信任评估模型[J].武汉大学学报(理学版),2006,52(5):627-630. 被引量:17
  • 4张宏,贺也平,石志国.一个支持空间上下文的访问控制形式模型[J].中国科学(E辑),2007,37(2):254-271. 被引量:21
  • 5SWEENEY L. ^-anonymity: a model for protecting privacy[ J ]. Inter-national Journal on Uncertainty, Fuzziness and Knowledge-based Systems,2002,10(5) :557-570.
  • 6SWEENEY L. Achieving A>anonymity privacy protection using gener-alization and suppression[ J]. International Journal on Uncertainty,Fuzziness and Knowledge-based Systems, 2002,10(5) : 571-588.
  • 7Li Ning-hui, LI Tian-cheng, VENKATASUBRAMANIAN S. (-closeness :privacy beyond A:-anonymity and /-diversity [ C ] //Proc of the 23rd International Conference on Data Engineering. Washington DC: IEEE Computer Society ,2007 :106-115.
  • 8MACHANAVAJJHALA A,KIFER D, GEHRKE J, et al. /-diversity; privacy beyond A:-anonymity [ C ] //Proc of the 22nd International Conference on Data Engineering. Washington DC:IEEE Computer Society,2006 :24-35.
  • 9CORMODE G,PROCOPIUC M,SRIVASTAVA D. et aL Differentially private publication of sparse data [ J ]. ArxiV Preprint arXiv : 1103. 0825,2011.
  • 10SARATHY R,MURALIDHAR K. Some additional insights on applying differential privacy for numeric data [ C ]//Proc of International Conference on Privacy in Statistical Databases. Berlin : Springer-Ver-lag,2010:210-219.

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