北京大学学报(自然科学版)

一种实现OLAP数据隐私保护的方法

陶有东,童云海,谭少华,唐世渭,杨冬青   

  1. 视觉与听觉处理教育部重点实验室北京大学,北京100871;
  • 收稿日期:2007-08-06 出版日期:2008-09-20 发布日期:2008-09-20

An Efficient Privacy Preserving Method in OLAP

TAO Youdong, TONG Yunhai, TAN Shaohua, TANG Shiwei, YANG Dongqing   

  1. Key Laboratory of Machine Perception Peking University, Ministry of Education, Beijing 100871;
  • Received:2007-08-06 Online:2008-09-20 Published:2008-09-20

摘要: 提出一种对于联机分析处理(OLAP)数据的隐私保护方法。首先采用多项式回归的方法对查询数据进行初步模拟,在初步模拟基础上提出了两类有效的规则进行优化处理。该方法在隐私保护的同时保持了较好的信息有效性。同时利用Kullback-Leibler信息量来描述OLAP模拟数据的信息有效性,解决了有效性难以评价的问题。实验表明该方法在隐私保护和信息有效性两个方面均取得了满意的结果。

关键词: 隐私保护, 联机分析处理, 多项式回归

Abstract: An optimized privacy preservation method for OLAP data is presented First the method simulates the original result data of query using polynomial regression Then two efficient rules are adopted to optimize the simulated result Kullback-Leibler divergence is adopted to measure the information utility which is hard to evaluate Experimental results indicate that this method gets the satisfying efficiency on the balance of the privacy and the utility

Key words: privacy preservation, OLAP, regression

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