SLOMS: A Privacy Preserving Data Publishing Method for Multiple Sensitive Attributes Microdata

Multi-dimension bucketization is a typical method to anonymize multiple sensitive attributes. However, the method leads to low data utility when microdata have more sensitive attributes. In addition, the methods do not generalize quasi-identifiers, which make the anonymous data vulnerable to suffer from linked attacks. To address the problems, the paper proposes a SLOMS method. The method vertically partitions the multiple sensitive attributes into several tables and bucketizes each sensitive attribute table to implement l-diversity. At the same time, it generalizes the quasi-identifiers to implement k-anonymity. The paper also proposes a MSB-KACA algorithm to anonymize microdata with multiple sensitive attributes by SLOMS. Experiments show that SLOMS can generate anonymous tables with less suppression ratio and less distortion compared with generalization and MSB.

[1]  ASHWIN MACHANAVAJJHALA,et al.  L-diversity: privacy beyond k-anonymity , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[2]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[3]  Pierangela Samarati,et al.  Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression , 1998 .

[4]  Girish Agarwal,et al.  A Review On Data Anonymization Technique For Data Publishing , 2012 .

[5]  Huiqun Yu,et al.  A Complete (alpha,k)-Anonymity Model for Sensitive Values Individuation Preservation , 2008, 2008 International Symposium on Electronic Commerce and Security.

[6]  Ninghui Li,et al.  Slicing: A New Approach for Privacy Preserving Data Publishing , 2009, IEEE Transactions on Knowledge and Data Engineering.

[7]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[8]  Ninghui Li,et al.  t-Closeness: Privacy Beyond k-Anonymity and l-Diversity , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[9]  Raymond Chi-Wing Wong,et al.  Achieving k-Anonymity by Clustering in Attribute Hierarchical Structures , 2006, DaWaK.

[10]  Yang Xiao.  Privacy Preserving Approaches for Multiple Sensitive Attributes in Data Publishing , 2008 .

[11]  Pierangela Samarati,et al.  Generalizing Data to Provide Anonymity when Disclosing Information , 1998, PODS 1998.

[12]  Yufei Tao,et al.  ANGEL: Enhancing the Utility of Generalization for Privacy Preserving Publication , 2009, IEEE Transactions on Knowledge and Data Engineering.

[13]  N. Revathi,et al.  Enhancing the Utility of Generalization for Privacy Preserving Re-publication of Dynamic Datasets , 2011 .

[14]  Yufei Tao,et al.  Anatomy: simple and effective privacy preservation , 2006, VLDB.