A Personalized Privacy Anonymous Method Based on Inverse Clustering

WANG Bo; YANG Jing

doi:10.3969/j.issn.0372-2112.2012.05.004

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A Personalized Privacy Anonymous Method Based on Inverse Clustering

更新时间：2025-07-16

- A Personalized Privacy Anonymous Method Based on Inverse Clustering
- Acta Electronica Sinica Vol. 40, Issue 5, Pages: 883-890(2012)
- 作者机构：
  
  1. 哈尔滨工程大学计算机科学与技术学院,黑龙江,哈尔滨,150001
  2. 哈尔滨理工大学自动化学院,黑龙江,哈尔滨,150080
  3. 哈尔滨工程大学计算机科学与技术学院,黑龙江,哈尔滨,150001
  4. 哈尔滨理工大学自动化学院,黑龙江,哈尔滨,150080
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2012.05.004
  CLC： TP309.2
- Published：2012
- 稿件说明：
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WANG Bo, YANG Jing. A Personalized Privacy Anonymous Method Based on Inverse Clustering[J]. Acta Electronica Sinica, 2012, 40(5): 883-890.
DOI：

WANG Bo, YANG Jing. A Personalized Privacy Anonymous Method Based on Inverse Clustering[J]. Acta Electronica Sinica, 2012, 40(5): 883-890. DOI： 10.3969/j.issn.0372-2112.2012.05.004.

摘要

针对不同个体对隐私保护的不同需求

提出了一种面向个体的个性化扩展

-多样性隐私匿名模型.该模型在传统

-多样性的基础上

定义了扩展的

-多样性原则

并通过设置敏感属性的保护属性来实现个体与敏感值之间关联关系的个性化保护需求.同时

还提出了一种个性化扩展

-多样性逆聚类(PELI-clustering)算法来实现该隐私匿名模型.实验表明:该算法不仅能产生与传统基于聚类的

-多样性算法近似的信息损失量以及更小的时间代价

同时也满足了个性化服务的需求

获得更有效的隐私保护.

Abstract

For achieving the different privacy preservation requirements of each individual

this paper presents a personalized extension

-diversity privacy anonymous model orienting individuals.This model proposes an extension

-diversity principle based on the traditional

-diversity

and realizes the requirement of personalized protection of relationship between individual and sensitive value by setting up guarding attributes on sensitive attributes.In the meantime

this paper also proposes a personalized extension

-diversity inverse clustering algorithm (PELI-clustering) to implement the privacy anonymous model presented in this paper.The experiments show that the proposed algorithm in this paper not only me

ets the requirements of personalized service

but also produces similar information loss to the traditional clustering-based

-diversity algorithm with less time cost

which achieves more effective privacy preservation.

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Individuation Privacy Preservation Oriented to Sensitive Values

Achieving (α,k)-Anonymity via Clustering in Data Publishing

A Multi Sensitive Attribute Data Inverse Clustering Privacy Preserving Algorithm for Sensitivity Attack

A Privacy Preserving Approach Based on Attributes Correlation Partition for Multiple Sensitive Attributes

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