Information Entropy Metric Methods of Association Attributes for Differential Privacy

WU Ning-bo; PENG Chang-gen; MOU Qi-lin

doi:10.3969/j.issn.0372-2112.2019.11.015

您当前的位置：

首页 >

文章列表页 >

Information Entropy Metric Methods of Association Attributes for Differential Privacy

更新时间：2025-07-16

- Information Entropy Metric Methods of Association Attributes for Differential Privacy
- Acta Electronica Sinica Vol. 47, Issue 11, Pages: 2337-2343(2019)
- 作者机构：
  
  1. 中电科大数据研究院有限公司,贵州,贵阳,550022
  2. 公共大数据国家重点实验室,贵州,贵阳,550025
  3. 贵州大学计算机科学与技术学院,贵州,贵阳,550025
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.11.015
  CLC： TP309
- Published Online：25 November 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
WU Ning-bo, PENG Chang-gen, MOU Qi-lin. Information Entropy Metric Methods of Association Attributes for Differential Privacy[J]. Acta Electronica Sinica, 2019, 47(11): 2337-2343.
DOI：

WU Ning-bo, PENG Chang-gen, MOU Qi-lin. Information Entropy Metric Methods of Association Attributes for Differential Privacy[J]. Acta Electronica Sinica, 2019, 47(11): 2337-2343. DOI： 10.3969/j.issn.0372-2112.2019.11.015.

摘要

针对差分隐私非交互式多属性关联的合成数据集发布问题，基于信息熵、汉明失真提出了发布数据集隐私度、数据效用、隐私泄露风险的量化方法.首先，利用互信息量分析属性相关度，并以关联依赖图模型表达属性关联.其次，基于图中关键隐私泄露路径构建马尔可夫隐私泄露链，并结合信息熵提出一种关联属性隐私度量模型及方法，可以有效的度量由关联属性引起的隐私泄露量.最后，通过具体实例验证了模型与方法的有效性，并对比分析了该方法的优势.

Abstract

Privacy leakage and utility measurement are widely concerned issues in multi-attribute datasets by non-interactive differential privacy publishing. In this paper

we have proposed several quantification methods by using information entropy and hamming distortion to quantify the privacy of published dataset

utility of dataset and risk of privacy leakage. First

we have tailored the existing mutual information concept to analyze the relationship among associated attributes and constructed an associated dependency graph model to analyze their correlations among multi-attribute. After that

we have developed a privacy quantification method based on information entropy and privacy leakage Markov chain

which is generated based on the graph of privacy leakage path that has a valid efficiency measurement of the privacy leakage leading by associated attributes. Finally

to justify the efficiency of the proposed model

we have included an illustrative example and demonstrated the advantage of our method by comparing with other methods.

关键词

Keywords

references

Views

144

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Differentially Private with Sparse and Smooth Self-Distillation

Differential Privacy-Based Double Energy Auction Privacy-Preserving on Consortium Blockchain

Research on the Deep Learning Method Based on Data Feature Relevance and Adaptive Differential Privacy

Track Privacy Protection Scheme Based on Adaptive Continuous Time in Crowdsensing

Research on Time-Serial Location Data Publication Based on Local Differential Privacy

Related Author

ZHAO Deng-feng

XUE Da-xuan

ZHAO Su-yun

CHEN Hong

JIANG Shun-rong

SHI Kun

ZHOU Yong

KANG Hai-yan

Related Institution

School of Information, Renmin University of China

Mine Digitization Engineering Research Center of the Ministry of Education

School of Computer Science and Technology, China University of Mining and Technology

Information Security Department， Beijing Information Science and Technology University

School of Computer Science， Hunan University of Technology and Business

⁰