MapReduce框架下支持差分隐私保护的k-means聚类方法

李洪成; 吴晓平; 陈燕

doi:10.11959/j.issn.1000-436x.2016038

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MapReduce框架下支持差分隐私保护的k-means聚类方法

学术论文 | 更新时间：2024-06-05

- MapReduce框架下支持差分隐私保护的k-means聚类方法
- k-means clustering method preserving differential privacy in MapReduce framework
- 通信学报 2016年37卷第2期页码：125-131
- 作者机构：
  
  1. 海军工程大学信息安全系，湖北武汉 430033
  2. 解放军 61062部队，北京100091
- 作者简介：
  
  [ "李洪成（1991-），男，河南商丘人，海军工程大学博士生，主要研究方向为信息安全、数据挖掘。" ]
  [ "吴晓平（1961-），男，山西新绛人，海军工程大学教授、博士生导师，主要研究方向为信息安全、密码学。" ]
  [ "陈燕（1975-），女，河北石家庄人，解放军61062部队高级工程师，主要研究方向为网络应用、信息系统。" ]
- 基金信息：
  
  国家自然科学基金青年基金资助项目(61100042);总后军内科研基金资助项目(AWS14R013)
- DOI：10.11959/j.issn.1000-436x.2016038
  中图分类号： TP301
- 网络出版日期：2016-02，
  
  纸质出版日期：2016-02-15
- 稿件说明：
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李洪成, 吴晓平, 陈燕. MapReduce框架下支持差分隐私保护的k-means聚类方法[J]. 通信学报, 2016,37(2):125-131.

Hong-cheng LI, Xiao-ping WU, Yan CHEN. k-means clustering method preserving differential privacy in MapReduce framework[J]. Journal on communications, 2016, 37(2): 125-131.
李洪成, 吴晓平, 陈燕. MapReduce框架下支持差分隐私保护的k-means聚类方法[J]. 通信学报, 2016,37(2):125-131. DOI： 10.11959/j.issn.1000-436x.2016038.

Hong-cheng LI, Xiao-ping WU, Yan CHEN. k-means clustering method preserving differential privacy in MapReduce framework[J]. Journal on communications, 2016, 37(2): 125-131. DOI： 10.11959/j.issn.1000-436x.2016038.

摘要

针对传统隐私保护方法无法应对任意背景知识下恶意分析的问题，提出了分布式环境下满足差分隐私的k-means算法。该算法利用MapReduce计算框架，由主任务控制k-means迭代执行；指派Mapper分任务独立并行计算各数据片中每条记录与聚类中心的距离并标记其属于的聚类；指派Reducer分任务计算同一聚类中的记录数量num和属性向量之和sum，并利用Laplace机制产生的噪声扰动num和sum，进而实现隐私保护。根据差分隐私的组合特性，从理论角度证明整个算法满足e差分隐私保护。实验结果证明了该方法在提高隐私性和时效性的-情况下，保证了较好的可用性。

Abstract

Aiming at the problem that traditional privacy preserving methods were unable to deal with malign analysis with arbitrary background knowledge

a k -means algorithm preserving differential privacy in distributed environment was proposed. This algorithm was under the computing framework of MapReduce. The host tasks were obligated to control the iterations of k -means. The Mapper tasks were appointed to compute the distances between all the records and cluster-ing centers and to mark the records with the clusters which the records belong. The Reducer tasks were appointed to compute the numbers of records which belong to the same clusters and the sums of attributes vectors

and to disturb the numbers and the sums with noises made by Laplace mecha ism

in order to achieve differential privacy preserving. Based on the combinatorial features of differential privacy

theoretically prove that this algorithm is able to fulfill -differentiallye private. The experimental results demonstrate that this method can remain available in the process of preserving privacy and improving efficiency.

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references

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