Access control model for multi-source heterogeneous data in big data environment

Qiuyue SU; Xingshu CHEN; Yonggang LUO

doi:10.11959/j.issn.2096-109x.2019009

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Access control model for multi-source heterogeneous data in big data environment

Papers | 更新时间：2024-06-03

- Access control model for multi-source heterogeneous data in big data environment
- Chinese Journal of Network and Information Security Vol. 5, Issue 1, Pages: 78-86(2019)
- 作者机构：
  
  1. 四川大学计算机学院，四川成都 610065
  2. 四川大学网络空间安全学院，四川成都 610065
  3. 四川大学网络空间安全研究院，四川成都 610065
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2019009
  CLC： TP309.2
- Online First：2019-02，
  
  Published：15 February 2019
- 稿件说明：
移动端阅览
Qiuyue SU, Xingshu CHEN, Yonggang LUO. Access control model for multi-source heterogeneous data in big data environment[J]. Chinese Journal of Network and Information Security, 2019, 5(1): 78-86.
DOI：

Qiuyue SU, Xingshu CHEN, Yonggang LUO. Access control model for multi-source heterogeneous data in big data environment[J]. Chinese Journal of Network and Information Security, 2019, 5(1): 78-86. DOI： 10.11959/j.issn.2096-109x.2019009.

摘要

大数据平台具有开放性和共享性，但随着数据量不断增加且用户访问上下文环境复杂多变，RBAC模型难以满足大数据环境下细粒度、灵活的访问控制。针对这一问题，提出了大数据平台下多源异构数据的访问控制模型。该模型根据属性动态地确定角色权限，并构建基于数据组的层次结构，实现数据属性的简单管理。对该模型进行了形式化定义，阐述了在 Hadoop 平台中的实现和工作流程，并通过实验验证了所提方案的性能开销相对较小。

Abstract

The big data platform is open and shared

but with the increasing amount of data and the complex and variable user access context

the RBAC model is difficult to meet the fine-grained and flexible access control in big data environment.To solve this problem

an access control model for multi-source heterogeneous data in big data platform is proposed.The model dynamically determines role permissions based on attributes and builds a hierarchical structure based on data groups to achieve simple management of data attributes.The model is formally defined

and the implementation and workflow in Hadoop platform are described.The experimental results show that the performance overhead of the proposed scheme is relatively small.

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references

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