基于聚类匿名化的差分隐私保护数据发布方法

刘晓迁; 李千目

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

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基于聚类匿名化的差分隐私保护数据发布方法

学术论文 | 更新时间：2024-06-05

- 基于聚类匿名化的差分隐私保护数据发布方法
- Differentially private data release based on clustering anonymization
- 通信学报 2016年37卷第5期页码：125-129
- 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：
  
  [ "刘晓迁（1989-），女，河北保定人，南京理工大学博士生，主要研究方向为数据挖掘、机器学习与隐私保护等。" ]
  [ "李千目（1979-），男，江苏南京人，博士，南京理工大学教授、博士生导师，主要研究方向为信息安全、传感网技术、智能决策与数据挖掘等。" ]
- 基金信息：
  
  中央高校基本科研业务专项基金资助项目(3091605104);国家自然科学基金资助项目(61272419);江苏省未来网络前瞻性研究基金资助项目(BY2013095-3-02);江苏省产学研前瞻性基金资助项目(BY2014089);江苏省产学研前瞻性基金资助项目(BY2013039);江苏省产学研前瞻性基金资助项目(BY2013037);江苏省普通高校研究生创新计划基金资助项目(KYLX15_0384)
- DOI：10.11959/j.issn.1000-436x.2016100
  中图分类号： TP392
- 网络首发：2016-05，
  
  纸质出版：2016-05-15
- 稿件说明：
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刘晓迁, 李千目. 基于聚类匿名化的差分隐私保护数据发布方法[J]. 通信学报, 2016,37(5):125-129.

Xiao-qian LIU, Qian-mu LI. Differentially private data release based on clustering anonymization[J]. Journal on Communications, 2016, 37(5): 125-129.
刘晓迁, 李千目. 基于聚类匿名化的差分隐私保护数据发布方法[J]. 通信学报, 2016,37(5):125-129. DOI： 10.11959/j.issn.1000-436x.2016100.

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.

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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 .

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