大数据云环境下TDS和BUG混合k-匿名化方法

范晓峰; 闫凤; 刘洋

doi:10.11959/j.issn.1000-0801.2016135

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大数据云环境下TDS和BUG混合k-匿名化方法

研究与开发 | 更新时间：2024-06-05

- 大数据云环境下TDS和BUG混合k-匿名化方法
- Hybrid k-anonymity approach based on TDS and BUG under the environment of big data cloud
- 电信科学 2016年32卷第7期页码：90-96
- 作者机构：
  
  1. 内蒙古商贸职业学院，内蒙古呼和浩特010070
  2. 内蒙古农业大学职业技术学院，内蒙古包头014109
  3. 呼和浩特职业学院，内蒙古呼和浩特010050
- 作者简介：
  
  [ "范晓峰（1982-），女，内蒙古商贸职业学院讲师，主要研究方向为大数据、云计算。" ]
  [ "闫凤（1982-），女，内蒙古农业大学职业技术学院讲师，主要研究方向为大数据。" ]
  [ "刘洋（1975-），女，呼和浩特职业学院讲师，主要研究方向为大数据。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2016135
  中图分类号： TP391
- 网络出版日期：2016-07，
  
  纸质出版日期：2016-07-15
- 稿件说明：
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范晓峰, 闫凤, 刘洋. 大数据云环境下TDS和BUG混合k-匿名化方法[J]. 电信科学, 2016,32(7):90-96.

Xiaofeng FAN, Feng YAN, Yang LIU. Hybrid k-anonymity approach based on TDS and BUG under the environment of big data cloud[J]. Telecommunications science, 2016, 32(7): 90-96.
范晓峰, 闫凤, 刘洋. 大数据云环境下TDS和BUG混合k-匿名化方法[J]. 电信科学, 2016,32(7):90-96. DOI： 10.11959/j.issn.1000-0801.2016135.

Xiaofeng FAN, Feng YAN, Yang LIU. Hybrid k-anonymity approach based on TDS and BUG under the environment of big data cloud[J]. Telecommunications science, 2016, 32(7): 90-96. DOI： 10.11959/j.issn.1000-0801.2016135.

摘要

针对一般子树匿名化方法处理大数据效率低和伸缩性较差的问题，提出了一种可伸缩的自下向上的泛化（BUG）方法，并在此基础上，结合已有的自上向下的特化（TDS），形成一种混合方法。在提出的方法中，k-匿名作为隐私模型，TDS和BUG都是基于映射化简开发组成，并通过云的强大计算能力来获得较高的伸缩性。提出的映射化简BUG只需在几次泛化循环之后就可插入一个新的泛化候选，不会影响另一个泛化的信息损失。考虑到工作负载平衡点K与匿名参数k的复杂关系，将映射化简的BUG和TDS结合形成混合方法。实验结果验证了本文方法的有效性，与TDS和BUG相比，混合方法的效率和可伸缩性大为提高。

Abstract

As the issue of low efficiency and poor scalability in general sub-tree anonymous method of treating big data

a bottom-up generalization(BUG) method with scalability was proposed

and on this basis

combined with the existing top-down specialization(TDS)

a hybrid approach was formed.In the proposed method

k-anonymity was being as a privacy model

the compositions of TDS and BUG were developed with mapping simplification

and higher scalability through powerful cloud computing capabilities were achieved.The proposed mapping simplification BUG could insert a new candidate after several cycles of generalization

and would not affect information loss of another generalization.Given the complexity of the relationship between workload balancing point K and anonymous parameter k

mapping simplifications of BUG and TDS were combined to form a hybrid approach.Experimental results demonstrate the effectiveness of the proposed method and compared with TDS and BUG

the efficiency and scalability of hybrid method are greatly improved.

关键词

Keywords

references

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