Transparent Link: A Framework of Anonymizing MSA-Dataset Based on Probabilistic Graphical Model

Privacy preserving of multi-sensitive attributes datasets (MSA-Datasets) has received increasing attention because of its huge social and economic benefits. In this paper, we introduce a novel and general privacy framework called Transparent Link. The Transparent Link framework can be used to anonymize MSA-Datasets by designing an algorithm based on probabilistic graphical model, which is referred to as APGM. Under the framework, to privately protect the relationships among multiple sensitive attributes, we present a clustering approach which can improve the utility of association rules through probabilistic edge association based on multipartite graphs. Experimental results show that our approach offer strong tradeoffs between privacy and utility.

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

[2]  Dongqing Yang,et al.  Identity-Reserved Anonymity in Privacy Preserving Data Publishing: Identity-Reserved Anonymity in Privacy Preserving Data Publishing , 2010 .

[3]  Manish Sharma,et al.  An efficient approach for privacy preserving in data mining , 2014, 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014).

[4]  Jianqiang Li,et al.  A hybrid solution for privacy preserving medical data sharing in the cloud environment , 2015, Future Gener. Comput. Syst..

[5]  Minyong Shi,et al.  Privacy Protection Method for Multiple Sensitive Attributes Based on Strong Rule , 2015 .

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

[7]  Jean-Pierre Corriveau,et al.  Geographic Partitioning Techniques for the Anonymization of Health Care Data , 2015, ArXiv.

[8]  Joshua Zhexue Huang,et al.  Rating: Privacy Preservation for Multiple Attributes with Different Sensitivity Requirements , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[9]  Yu Liu,et al.  Decomposition: Privacy Preservation for Multiple Sensitive Attributes , 2009, DASFAA.

[10]  Cynthia Dwork.  International Conference on Theory and Applications of Models of Computation , 2008 .

[11]  Hao Peng,et al.  SLOMS: A Privacy Preserving Data Publishing Method for Multiple Sensitive Attributes Microdata , 2013, J. Softw..

[12]  Tong Yun.  Identity-Reserved Anonymity in Privacy Preserving Data Publishing , 2010 .

[13]  Ashwin Machanavajjhala,et al.  l-Diversity: Privacy Beyond k-Anonymity , 2006, ICDE.

[14]  Pierangela Samarati,et al.  Protecting Respondents' Identities in Microdata Release , 2001, IEEE Trans. Knowl. Data Eng..

[15]  See-Kiong Ng,et al.  Privacy beyond Single Sensitive Attribute , 2011, DEXA.

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

[17]  S. Saraswathi,et al.  Enhancing Utility and Privacy using t-closeness For Multiple Sensitive Attributes , 2016 .

[18]  Yang Jing and Wang Bo.  Personalized l-Diversity Algorithm for Multiple Sensitive Attributes Based on Minimum Selected Degree First , 2012 .

[19]  Cynthia Dwork,et al.  Differential Privacy: A Survey of Results , 2008, TAMC.

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

[21]  Jiajia Zhang,et al.  MADARS: A Method of Multi-Attributes Generalized Randomization Privacy Preserving , 2015, MUE 2015.