Research on k-anonymity Algorithm for Personalized Quasi-identifier Attributes

doi:10.3969/j.issn.1671-1122.2020.10.003

Abstract

Abstract:

k-anonymity can solve the problem of link attack in the field of privacy protection to a great extent, but the existing k-anonymity model does not attach importance to personal privacy autonomy. The existing improved k-anonymity model can not meet the needs of different people for different types of data. After the data table is published, the whole table still has only one k value, that is, all tuples are unified and generalized, which can not reflect the user's personalized privacy requirements, resulting in great information loss. Based on k-anonymity model, combined with the generalization idea based on clustering, this paper proposes a k-anonymity algorithm for personalized quasi-identifier attributes(KAUP). The algorithm can effectively present different k values on the same data table according to the privacy requirements of users, so as to meet the personalized k-anonymity. This paper designs comparative experiments of runtime, information loss and scalability on dataset Adult. Experiments show that personalized anonymity on the same data table is feasible, and the information loss in the anonymity process is small, which is conducive to the personalized anonymity research of quasi-identifier attributes.

Key words: personalization, k-anonymity, privacy protection

CLC Number:

TP309

HE Jingsha, DU Jinhui, ZHU Nafei. Research on k-anonymity Algorithm for Personalized Quasi-identifier Attributes[J]. Netinfo Security, 2020, 20(10): 19-26.

Figures/Tables 8

References 24

[1]	LIU Ming, YE Xiaojun. Personalized K-anonymity[J]. Computer Engineering and Design, 2008,29(2):282-286.
	刘明, 叶晓俊. 个性化K-匿名模型[J]. 计算机工程与设计, 2008,29(2):282-286.
[2]	YANG Jing, REN Xiangmin, ZHANG Jianpei, et al. Research on Uncertain Data Privacy Protection Based on k-anonymity[J]. International Journal of Advancements in Computing Technology, 2012,4(15):140-149.
[3]	NI Sang, XIE Mengbo, QIAN Quan. Clustering-based k-anonymity Algorithm for Privacy Preservation[J]. International Journal of Network Security, 2017,6(19):1062-1071.
[4]	FANG Yu, GAO Lei, LIU Zhonghui, et al. Generalized Cost-sensitive Approximate Attribute Reduction Based on Three-way Decisions[J]. Journal of Nanjing University of Science and Technology, 2019,43(4):481-488.
	方宇, 高磊, 刘忠慧, 等. 基于三支决策的广义代价敏感属性约简[J]. 南京理工大学学报, 2019,43(4):481-488.
[5]	YE Xiaojun, ZHANG Yawei, LIU Ming. A Personalized (α-k)-anonymity Model [C]// IEEE. The 9th International Conference on Web-Age Information Management, July 20-22, 2008, Zhangjiajie, China. NJ: IEEE, 2008: 341-348.
[6]	YIN Yanmin. Research on Privacy Preserving Method of Location Service Trajectory in Fog Computing[J]. Qufu: Qufu Normal University, 2019.
	尹彦民. 雾计算中位置服务轨迹隐私保护方法研究[D]. 曲阜:曲阜师范大学, 2019.
[7]	SWEENEY L. k-anonymity: A Model for Protecting Privacy[J]. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002,10(5):557-570.
[8]	LIU Xiangwen, XIE Qingqing, WANG Liangmin. Personalized Extended (α, k)-anonymity Model for Privacy-preserving Data Publishing[EB/OL]. https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.3886, 2016-6-30.
[9]	SHEN Yanguang, GUO Gaoshang, WU Di, et al. A Novel Algorithm of Personalized-granular k-anonymity [C]//IEEE. 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer, December 20-22, 2013, Shenyang, China. NJ: IEEE, 2013: 1860-1866.
[10]	LIU Yinghua, YANG Bingru, LI Guangyuan. A Personalized Privacy Preserving Parallel (alpha, k)-anonymity Model[J]. International Journal of Advanced in Computing Technology, 2012,5(4):165-271.
[11]	LIAO Jun, JIANG Chaohui, GUO Chun, et al. Classification Anonymity Algorithm Based on Weight Attributes Entropy[J]. Computer Science, 2017,44(7):42-46.
	廖军, 蒋朝惠, 郭春, 等. 一种基于权重属性熵分类的匿名算法[J]. 计算机科学, 2017,44(7):42-46.
[12]	REN Xiangmin, YANG Jing, WEI Fengmei. Research on Diversity of Sensitive Attribute of K-anonymity [C]//IEEE. 2010 2nd International Workshop on Database Technology and Application, November 27-28, 2010, Wuhan, China. NJ: IEEE, 2010: 1-4.
[13]	YU Shoujian, NIU in, YANG Yun, et al. Personalized Privacy Protection Based on Diversity against Connection Fingerprint Attact [C]//IEEE. 2019 IEEE International Conference on Power, Intelligent Computing and Systems, July 12-14,2019, Shenyang, China. NJ: IEEE, 2019: 205-211.
[14]	TRUTA T M, CAMPAN A, MEYER P. Generating Microdata with p-sensitive k-anonymity Property[M] //Springer. Secure Data Management. Heidelberg: Springer, Berlin, Heidelberg, 2007: 124-141.
[15]	JIA Junjie, YAN Guolei. A Personalized (p, k)-Anonymity Privacy Protection Algorithm[J]. Computer Engineering, 2018,44(1):176-181.
	贾俊杰, 闫国蕾. 一种个性化(p,k)匿名隐私保护算法[J]. 计算机工程, 2018,44(1):176-181.
[16]	ZHAO Yan, WANG Jian, LUO Yongcheng, et al. (α, β, k)-anonymity: An Effective Privacy Preserving Model for Databases [C]//IEEE. 2009 International Conference on Test and Measurement. December 5-6, 2009, Hong Kong, China. NJ: IEEE, 2009: 412-415.
[17]	RAO U P, MEHTA B B, KUMAR N. Scalable L-diversity: An Extension to Scalable k-anonymity for Privacy Preserving Big Data Publishing[J]. International Journal of Information Technology and Web Engineering, 2019, 14(2): 27-40.
[18]	LIAO Jun. Research on Data Privacy Protection Method Based on Classified Mining[D]. Guiyang: Guizhou University, 2017.
[19]	TELL R. Property Testing Lower Bounds Via a Generalization of Randomized Parity Decision Trees[J]. Theory of Computing Systems, 2019,63(15):418-449.
[20]	ZENG Kai. Privacy Protection Model Research[D]. Chongqing: Chongqing University, 2012.
[21]	YU Juan. Research on Anonymity Model and Algorithm of Privacy Protection in Data Publishing[D]. Jinhua: Zhenjiang Normal University, 2010.
	于娟. 数据发布中隐私保护的匿名模型及算法研究[D]. 金华：浙江师范大学,2010.
[22]	ZENG Mengjia, CHENG Zhaolin, HUANG Xu, et al. Spatial Crowdsourcing Quality Control Model Based on K-Anonymity Location Privacy Protection and ELM Spammer Detection[J]. Mobile Information Systems, 2019,4(10):1-10.
[23]	SONG Jinling, LIU Guohua, HUANG Liming, et al. k-Anonymous Privacy Protection Model K-Value Optimal Selection Algorithms[J]. Minicomputer System, 2011,32(10):1987-1993.
[24]	CAO Minzi, ZHANG Linlin, BI Xuehua, et al. Personalized (alpha, l)- Diversity K-Anonymous Privacy Protection Model[J]. Computer Science, 2018,45(11):180-186.

属性名称	取值个数	泛化树深度	属性类型
Sex	2	2	${{A}^{\text{QI}}}$
Country	40	5	${{A}^{\text{QI}}}$
Education	16	3	${{A}^{\text{QI}}}$
Occupation	14	3	${{A}^{\text{QI}}}$
maritalStatus	7	3	${{A}^{\text{QI}}}$