Research in Microaggregation Algorithms for k-Anonymization

HAN Jian-min; CEN Ting-ting; YU Hui-qun

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Research in Microaggregation Algorithms for k-Anonymization

更新时间：2025-07-16

- Research in Microaggregation Algorithms for k-Anonymization
- Acta Electronica Sinica Vol. 36, Issue 10, Pages: 2021-2029(2008)
- 作者机构：
  
  1. 华东理工大学计算机科学与工程系,上海,200237
  2. 浙江师范大学数理与信息工程学院,浙江,金华,321004
  3. 华东理工大学计算机科学与工程系上海,200237
  4. 浙江师范大学数理与信息工程学院浙江金华,321004
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP309.2
- Published：2008
- 稿件说明：
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HAN Jian-min, CEN Ting-ting, YU Hui-qun. Research in Microaggregation Algorithms for k-Anonymization[J]. Acta Electronica Sinica, 2008, 36(10): 2021-2029.
DOI：

HAN Jian-min, CEN Ting-ting, YU Hui-qun. Research in Microaggregation Algorithms for k-Anonymization[J]. Acta Electronica Sinica, 2008, 36(10): 2021-2029. DOI：

摘要

数据表的

-匿名化(

-anonymization)是数据发布时保护私有信息的一种重要方法.泛化/隐匿是实现

-匿名的传统技术

然而

该技术存在效率低、

-匿名化后数据的可用性差等问题.近年来

微聚集(Microaggregation)算法被应用到数据表的

-匿名化上

弥补了泛化/隐匿技术的不足

其基本思想是:将大量的数据按相似程度划分为若干类

要求每个类内元组数至少为

个

然后用类质心取代类内元组的值

实现数据表的

-匿名化.本文综述了微聚集算法的基本思想、相关技术和当前动态

对现有的微聚集算法进行了分类分析

并总结了微聚集算法的评估方法

最后对微聚集算法的研究难点及未来的发展趋势作了探讨.

Abstract

-anonymization of tables is a method to prevent private information from disclosure prior to publication

which is achieved traditionally via generalization/suppression techniques.However

these methods have some defects on efficiency

availability

etc.Recently

microaggregation algorithm is proposed as an alternative to generalization/suppression method for

-anonymization whose goal is to cluster a set of records into groups of size at least

such that groups are as homogeneous as possible.Then the records’attribute values in the same group are replaced by the group’s centroid.Microaggregation algorithms’core ideas

the state-of-the-art and related techniques are surveyed.The existing algorithms are classified and analyzed.

Evaluation methods of microaggregation algorithms are investigated.Finally

some open problems and the research directions in this area are discussed.

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