Differentially private data release based on clustering anonymization

Xiao-qian LIU; Qian-mu LI

doi:10.11959/j.issn.1000-436x.2016100

您当前的位置：

首页 >

文章列表页 >

Differentially private data release based on clustering anonymization

Papers | 更新时间：2024-06-05

- Differentially private data release based on clustering anonymization
- Journal on Communications Vol. 37, Issue 5, Pages: 125-129(2016)
- 作者机构：
  
  南京理工大学计算机科学与工程学院，江苏南京210094
- 作者简介：
- 基金信息：
  
  The Fundational Research Funds for the Central Universities(3091605104);The National Natural Science Foundation of China(61272419);The Future Network Prospective Study Project of Jiangsu Province(BY2013095-3-02);The Industry-University-Research Perspective Project of Jiangsu Province(BY2014089);The Industry-University-Research Perspective Project of Jiangsu Province(BY2013039);The Industry-University-Research Perspective Project of Jiangsu Province(BY2013037);Graduate Students Research Innovation Plan of Jiangsu Province(KYLX15_0384)
- DOI：10.11959/j.issn.1000-436x.2016100
  CLC： TP392
- Online First：2016-05，
  
  Published：15 May 2016
- 稿件说明：
移动端阅览
Xiao-qian LIU, Qian-mu LI. Differentially private data release based on clustering anonymization[J]. Journal on Communications, 2016, 37(5): 125-129.
DOI：

Xiao-qian LIU, Qian-mu LI. Differentially private data release based on clustering anonymization[J]. Journal on Communications, 2016, 37(5): 125-129. DOI： 10.11959/j.issn.1000-436x.2016100.

摘要

基于匿名化技术的理论基础，采用DBSCAN聚类算法对数据记录进行聚类，实现将个体记录匿名化隐藏于一组记录中。为提高隐私保护程度，对匿名化划分的数据添加拉普拉斯噪声，扰动个体数据真实值，以实现差分隐私保护模型的要求。通过聚类，分化查询函数敏感性，提高数据可用性。对算法隐私性进行证明，并实验说明发布数据的可用性。

Abstract

Based on the theory of anonymization

the DBSCAN method was applied to divide all the data records into different groups to cover individuals.To provide priv enhancement

the Laplace noise was added to the anonymized partitioned data to perturb the real value of data record so that the requirements of differential privacy model were satis-fied.With the clustering operation

the sensitivity of the query function has been partitioned to improve data utility.The proof of privacy has been given and experimental results have been provided to evaluate the utility of the released data.

关键词

Keywords

references

ADAM N R , WORTHMANN J C . Security-control methods for statis-tical databases:a comparative study [J ] . ACM Computing Surveys(CSUR) , 1989 , 21 ( 4 ): 515 - 556 .

LATANYA S . k-anonymity:a model for protecting privacy [J ] . Inter-national Journal on Uncertainty Fuzziness Knowledge-based Systems , 2002 , 10 ( 5 ): 557 - 570 .

DWORK C . Differential privacy [M ] . Encyclopedia of Cryptography and Security.Springer US , 2011 : 338 - 340 .

VERYKIOS V S , BERTINO E , FOVINO I N , et al . State-of-the-art in privacy preserving data mining [J ] . ACM Sigmod Record , 2004 , 33 ( 1 ): 50 - 57 .

DWORK C , NISSIM K . Privacy-preserving datamining on vertically partitioned databases [C ] // Advances in Cryptology–CRYPTO 2004 . California,USA:Springer Berlin Heidelberg , C2004 : 528 - 544 .

FRIEDMAN A , SCHUSTER A . Data mining with differential ivacy [C ] // The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . Washington DC,USA:ACM , C2010 : 493 - 502 .

CHAUDHURI K , MONTELEONI C , SARWATE A D . Differentially private empirical risk minimization [J ] . The Journal of Machine Learn-ing Research , 2011 , 12 : 1069 - 1109 .

OLIVEIRA S R M , ZAIANE O R . Privacy preserving clustering by data transformation [J ] . Journal of Information and Data Management , 2010 , 1 ( 1 ): 37 .

崇志宏 , 倪巍伟 , 刘腾腾 , 等 . 一种面向聚类的隐私保护数据发布方法 [J ] ．计算机研究与发展 , 2010 , 47 ( 12 ): 2083 - 2089 .

CHONG Z H , NI W W , LIU T T , et al . A privacy-preserving data pub-lishing algorithm for clustering application [J ] . Journal of Computer Research and Development , 2010 , 47 ( 12 ): 2083 - 2089 .

李杨 , 郝志峰 , 温雯 , 等 . 差分隐私保护k-means聚类方法研究 [J ] ．计算机科学 , 2013 , 40 ( 3 ): 287 - 290 .

LI Y , HAO Z F , WEN W , et al . Research on differential privacy preserv-ing k-means clustering [J ] . Computer Science , 2013 , 40 ( 3 ): 287 - 290 .

KUMAR K A , RANGAN C P . Privacy preserving DBSCAN algorithm for clustering [C ] // Advanced Data Mining and Applications . Harbin,China c2007 : 57 - 68 .

AMIRBEKYAN A , ESTIVILL-CASTRO V . Privacy preserving DBSCAN for vertically partitioned data [C ] // Intelligence and Security Informatics . San Diego,CA,USA:Springer Berlin Heidelberg , c2006 : 141 - 153 .

XU W , HUANG L , LUO Y , et al . Protocols for privacy-preserving DBSCAN clustering [J ] . Int J Secur , 2007 , 1 ( 1 ): 45 - 56 .

HAEBERLEN A , PIERCE B C , NARAYAN A . Differential privacy under fire [C ] // USENIX Security Symposium . San Francisco,CA,USA : c2011 .

DWORK C , MCSHERRY F , NISSIM K , et al . Calibrating noise to sensitivity in private data analysis [M ] . Theory of Cryptography.Springer Berlin Heidelberg , 2006 : 265 - 284 .

MCSHERRY F D . Privacy integrated queries:an extensibl platform for privacy-preserving data analysis [C ] // The 2009 ACM SIGMOD In-ternational Conference on Management of Data . Providence,Rhode Island:ACM , c2009 : 19 - 30 .

Views

2250

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Differential privacy protection scheme based on edge betweenness model

MAXGDDP:decision data release with differential privacy

Trajectory reconstruction attacks on differential privacy based on a CNN-BiLSTM-Attention hybrid model

Federated learning with differential privacy recalibration for dynamic computing nodes

Lightweight authentication key agreement protocol for Internet of vehicles

Related Author

Haiping HUANG

Kai WANG

Xiong TANG

Dongjun ZHANG

Jibin FU

Xiaojian ZHANG

Liping DING

XIE Lixia

Related Institution

College of Computer, Nanjing University of Posts and Telecommunications

High Technology Research Key Laboratory of Wireless Sensor Network of Jiangsu Province

College of Computer ＆Information Engineering,Henan University of Economics and Law

Institute of Software,Chinese Academy of Sciences

School of Computer Science and Technology, Civil Aviation University of China

AI问答

⁰