计算机科学 ›› 2018, Vol. 45 ›› Issue (12): 117-122.doi: 10.11896/j.issn.1002-137X.2018.12.018

• 信息安全 • 上一篇    下一篇

基于形式概念分析的语义角色挖掘算法

周超1,2,3,4, 任志宇1,2,3, 毋文超1,2,3   

  1. (信息工程大学 郑州450001)1
    (河南省信息安全重点实验室 郑州450001)2
    (数学工程与先进计算国家重点实验室 郑州450001)3
    (中国洛阳电子装备试验中心 河南 洛阳471003)4
  • 收稿日期:2017-11-22 出版日期:2018-12-15 发布日期:2019-02-25
  • 作者简介:周 超(1993-),男,硕士生,主要研究方向为信息安全,E-mail:zacharyvic@163.com;任志宇(1974-),女,博士,副教授,主要研究方向为信息安全,E-mail:zhiyu.ren@163.com(通信作者);毋文超(1995-),男,硕士生,主要研究方向为信息安全。
  • 基金资助:
    本文受国家自然科学基金(61702550,61502531),国家“八六三”高技术研究发展计划项目基金(SQ2015AA011705)资助。

Semantic Roles Mining Algorithms Based on Formal Concept Analysis

ZHOU Chao1,2,3,4, REN Zhi-yu1,2,3, WU Wen-chao1,2,3   

  1. (Information Engineering University,Zhengzhou 450001,China)1
    (Henan Province Key Laboratory of Information Security,Zhengzhou 450001,China)2
    (State Key Laboratory of Mathematical Engineering & Advanced Computing,Zhengzhou 450001,China)3
    (Electronic Equipment Test Cneter,Luoyang,Henan 471003,China)4
  • Received:2017-11-22 Online:2018-12-15 Published:2019-02-25

摘要: 基于角色的访问控制(Role-Based Access Control,RBAC)在管理和安全方面具有优势,经过20多年的发展后已被广泛应用于各个领域,如何将数据繁多的非RBAC系统迁移成RBAC系统已经成为一个意义重大的难题。角色是RBAC的基本特征,因此角色挖掘是RBAC系统实施的一个重要环节。基于形式概念分析生成用户权限概念格及用户属性概念格,将用户权限概念格翻转后映射为初始候选角色状态,通过约简操作和精简操作来挖掘角色,然后对用户权限概念格及用户属性概念格进行相似性分析,通过定义最近似表达式为角色赋予语义,使得生成的角色具有以下两点优势:1)结构层次,有效地减轻了管理员授权的负担,提高了授权管理的效率;2)语义意义,能够与现实生活中的概念相关联,增强了角色的可解释性。最后,通过实验验证了该算法的正确性和有效性。

关键词: 概念格, 角色挖掘, 形式概念分析, 语义, 属性

Abstract: Role-based access control (RBAC) with the advantages of management and security has been widely used in various fields after more than 20 years of development.How to migrate a non-RBAC system with a variety of data into an RBAC system has become a significant problem.Role is a basic feature of RBAC,therefore,role mining is an important part of the implementation of RBAC system.In this paper,the user-permission concept lattice and user-attribute concept lattice were generated based on formal concept analysis.After the user-permission concept lattice was reversed,it was mapped to initial candidate role state,and the final role state was mined by reduction and pruning operations.And then,the most approximate expressions were defined to give semantic meanings to roles by analyzing the similarity between user-permission concept lattice and user-attribute concept lattice.The generated roles have two advantages,one is structural hierarchy,which effectively reduces the authorization burden of administrator,and the other one is semantic meanings,which can be associated with the concepts in real life,enhancing the interpretability of role.Finally,the expe-rimental results verify the correctness and effectiveness of the proposed algorithm.

Key words: Attribute, Concept lattice, Formal concept analysis, Role mining, Semantic meanings

中图分类号: 

  • TP309
[1]SANDHU R S,COYNE E J,FEINSTEINH L,et al.Role-based access control models[J].Computer,1996,29(2):38-47.
[2]COYNE E J.Role engineering[C]∥Proceedings of the first ACM Workshop on Role-based access control.ACM,1996.
[3]MITRA B,SURAL S,VAIDYA J,et al.A survey of role mining[J].ACM Computing Surveys (CSUR),2016,48(4):1-37.
[4]SCHLEGELMILCH J,STEFFENS U.Role mining with ORCA[C]∥Proceedings of the tenth ACM symposium on Access control Models and Technologies.ACM,2005:168-176.
[5]ZHANG D N,RAMAMOHANARAO K,EBRINGER T.Roleengineering using graph optimization[C]∥Proceedings of the 12th ACM Symposium on Access Control Models and Technologies.2007:139-144.
[6]GUO Q,VAIDYA J,ATLURI V.The role hierarchy miningproblem:discovery of optimal role hierarchies[C]∥Computer Security Applications Conference.IEEE,2008:237-246.
[7]SARMAH A K,HAZARIKA S M,SINHA S K.Formal concept analysis:current trends and directions[J].Artificial Intelligence Review,2015,44(1):47-86.
[8]MOLLOY I,CHEN H,LI T,et al.Mining roles with multiple objectives[J].ACM Transactions on Information and System Security (TISSEC),2010,13(4):1-35.
[9]SOBIESKI S,ZIELINSKI B.Modelling role hierarchy structure using the Formal Concept Analysis[J].Annales Umcs Informa-tica,2010,10(2):143-159.
[10]KUMAR C.Designing role-based access control using formalconcept analysis[J].Security and Communication Networks,2013,6(3):373-383.
[11]KUMAR C A,MOULISWARAN S C,LI J,et al.Role based access control design using triadic concept analysis[J].Journal of Central South University,2016,23(12):3183-3191.
[12]ZHANG L,ZHANG H L,HAN D J,et al.Theory and Algorithm for Roles Minimization Problem in RBAC Based on Concept Lattice[J].Acta Electronica Sinica,2014,42(12):2371-2378.(in Chinese)
张磊,张宏莉,韩道军,等.基于概念格的 RBAC 模型中角色最小化问题的理论与算法[J].电子学报,2014,42(12):2371-2378.
[13]GANTER B,WILLE R.Formal concept analysis:mathematical foundations[M].New York:Springer Science & Business Media,2012.
[14]ZHI H L.Extended Model of Formal Concept Analysis Oriented for Heterogeneous Data Analysis[J].Acta Electronica Sinica,2013,41(12):2451-2455.(in Chinese)
智慧来.面向异构数据分析的形式概念分析扩展模型[J].电子学报,2013,41(12):2451-2455.
[15]GODIN R,MINEAU G,MISSAOUI R,et al.Méthodes de classification conceptuelle basées sur les treillis de Galois et applications[J].Revued’Intelligence Artificielle,1995,9:105-137.
[16]GANTER B.Two Basic Algorithms in Concept Analysis[C]∥International Conference on Formal Concept Analysis.Springer-Verlag,2010:312-340.
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