计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 395-399.doi: 10.11896/JsJkx.190500131
张王策, 范菁, 王渤茹, 倪旻
ZHANG Wang-ce, FAN Jing, WANG Bo-ru and NI Min
摘要: 在数据集对外发布之前,需要对数据集的准标识符属性进行匿名,以防遭受链接攻击。然而现有的数据匿名算法都是面向完整数据进行,对于数据集中含有缺损数据的元组会进行直接删除操作,降低了数据的可用性。文中提出将缺损数据与完整数据混合匿名的算法,并且结合了(α,k)-匿名算法。实验得出的数据充分证明:改进后的面向缺损数据的(α,k)-匿名模型有效提升了匿名后数据的可用性,实现了数据匿名。
中图分类号:
[1] SWEENEY L.k-Anonymity:A model for protecting privacy .Int’l Journal on Uncertain,Fuzziness and Knowledge-Based Systems,2002,10(5):557-570. [2] SAMARATI P.Protecting respondents’identities in microdata release.IEEE Transactions on Knowledge and Data Engineering,2001,13(6):1010-1027. [3] LI T C,LI N H.Towards optimal k-anonymization.Data and Knowledge Engineering,2008,65(1):22 -39. [4] 韩建民,于娟,虞慧群,等.面向敏感值的个性化隐私保护.电子学报,2010,38(7):1723-1728. [5] MACHANAVAJJHALA A,GEHRKE J,KIFER D.L-diversity:privacy beyondk-anonymity//Proceedings of the 22nd International Conference on Data Engineering.Atlanta,GA,USA:IEEE Press,2006:24-36. [6] TRUTA T M,VINAY B.Privacy protection:p-sensitive kanonymityproperty//Proceedings of the 22nd International Conference on Data Engineering Workshops (ICDEW).Wa-shington,DC,USA:IEEE Computer Society,2006:94. [7] WONG C R,LI J,FU A,et al.(α,k)-anonymity:an enhancedk-anonymity model for privacy preserving data publishing//Proceedings of the 12th ACM SIGKDD Conference.Philadelphia,PA:ACM Press,2006:754-759. [8] LI N H,LI T C,VENKATASUBRAMANIAN S.t-Closeness:privacy beyond k-anonymity and l-diversity//Proceedingsof the 23rd International Conference on Data Engineering(ICDE).Istanbul,Turkey:IEEE Press,2007:106-115. [9] XIAO X K,TAO Y F.Personalized privacy preservation//Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data.Chicago,USA:ACM Press,2006:229-240. [10] YANG X C,LIU X Y,WANG B,et al.K-Anonymization approaches for supporting multiple constraints.Journal of Software,2006,17(5):1222-1231. [11] LEFEVRE K,DEWITT D J,RAMAKRISHNAN R.Incognito:Efficient full-domain K-anonymity//Proc.of the ACM SIGMOD Int’l Conf.on Management of Data (SIGMOD).ACM Press,2005:49-60. [12] XU J,WANG W,PEI J,et al.Utility-Based anonymization using local recoding//Proc.of the 12th ACMSIGKDD Int’l Conf.on Knowledge Discovery and Data Mining (SIGKDD).ACM Press,2006:785-790. [13] LEFEVRE K,DEWITT D J,RAMAKRISHNAN R.Mondrian multidimensional K-anonymity//Proc.of the 22nd Int’l Conf.on Data Engineering (ICDE).IEEE,2006:25. [14] XIAO XK,TAO Y.Anatomy:Simple and effective privacy preservation//Proc.of the 32nd Int’l Conf.on Very Large Data Bases(VLDB).VLDB Endowment,2006:139-150. [15] TAO YF,CHEN H K,XIAO X,et al.ANGEL:Enhancing the utility of generalization for privacy preserving publication.IEEE Trans.on Knowledge and Data Engineering (TKDE),2009,21(7):1073-1087. [16] WONG R C W,LI J Y,FU A W C,et al.(α,k)-Anonymity:An enhanced k-anonymity model for privacy preserving data publishing//Proc.of the 12th ACM SIGKDD Int’l Conf.on Knowledge discovery and Data Mining (SIGKDD).ACM Press,2006:754-759. [17] MACHANAVAJJHALA A,KIFER D,GEHRKE J,et al.l-Diversity:Privacy beyond k-anonymity.ACM Trans.on Knowledge Discovery Data (TKDD),2007,1:3. [18] 任向民.基于K-匿名的隐私保护方法研究.哈尔滨:哈尔滨工程大学,2012. |
[1] | 童海,白光伟,沈航. 基于双向拍卖的k-匿名激励机制 Double-auction-based Incentive Mechanism for k-anonymity 计算机科学, 2019, 46(3): 202-208. https://doi.org/10.11896/j.issn.1002-137X.2019.03.030 |
[2] | 曹敏姿, 张琳琳, 毕雪华, 赵楷. 个性化(α,l)-多样性k-匿名隐私保护模型 Personalized (α,l)-diversity k-anonymity Model for Privacy Preservation 计算机科学, 2018, 45(11): 180-186. https://doi.org/10.11896/j.issn.1002-137X.2018.11.028 |
|