计算机科学 ›› 2025, Vol. 52 ›› Issue (11A): 241200047-8.doi: 10.11896/jsjkx.241200047

• 信息安全 • 上一篇    下一篇

基于粒关联的数据聚合信息级别推演方法

李金辉1, 曹利峰1, 汪小芹2, 白金龙1, 陈阳1   

  1. 1 河南省信息安全重点实验室 郑州 450000
    2 中国电子科技集团公司第七研究所 广州 510277
  • 出版日期:2025-11-15 发布日期:2025-11-10
  • 通讯作者: 曹利峰(caolf302@sina.com)
  • 作者简介:jineh1214@163.com

Information Level Inference Method for Data Aggregation Based on Granular Association

LI Jinhui1, CAO Lifeng2, WANG Xiaoqin2, BAI Jinlong2, CHEN Yang2   

  1. 1 Henan Provincial Key Laboratory of Information Security,Zhengzhou 450000,China
    2 China Electronics Technology Group Corporation Seventh Research Institute,Guangzhou 510277,China
  • Online:2025-11-15 Published:2025-11-10

摘要: 为解决大数据聚合而引起敏感信息泄露的问题,对数据之间的关联性进行了深入的分析,提出了基于粒关联的数据聚合信息级别推演方法。根据数据属性的依赖关系,挖掘出高关联度的数据对象,进而依据数据对象关联属性的敏感级别模糊集可能性测度推演用户访问多信息系统时由数据聚合推导高敏感级别信息的可能性。这种方法有助于为用户制定数据访问策略,控制对关联数据的分析,降低信息泄露的风险。

关键词: 数据分析, 粒关联, 关联规则, 聚合推演, 信息泄露

Abstract: To address the issue of sensitive information leakage through the existence of big data aggregation,this study analyzes the correlation between data deeply and proposes an information level inference method for data aggregation based on granular association.The method mines highly associated data objects based on the dependencies of data attributes,and then deduces the possibility of inferring highly sensitive information from data aggregation when users access the multi-information system based on the fuzzy set possibility measurement of the sensitivity level of the associated attributes of the data objects.This approach aids in establishing access policies for users,controlling the control the analysis of associated data,and reducing the risk of information leakage.

Key words: Data analysis, Granular association, Association rules, Aggregation inference, Information leakage

中图分类号: 

  • TP309
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