International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 11, No. 4, 8 Apr. 2019
Cover page and Table of Contents: PDF (size: 1096KB)
A Novel Dynamic KCi - Slice Publishing Prototype for Retaining Privacy and Utility of Multiple Sensitive Attributes
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Author(s)
N.V.S. Lakshmipathi Raju
1,*
M.N. Seetaramanath
1
P.Srinivasa Rao
2
Index Terms
Novel KCi-slice, Data utility, Privacy, Slicing, High sensitive attribute, Low sensitive attribute
Abstract
Data publishing plays a major role to establish a path between current world scenarios and next generation requirements and it is desirable to keep the individuals privacy on the released content without reducing the utility rate. Existing KC and KCi models concentrate on multiple categorical sensitive attributes. Both these models have their own merits and demerits. This paper proposes a new method named as novel KCi - slice model, to enhance the existing KCi approach with better utility levels and required privacy levels. The proposed model uses two rounds to publish the data. Anatomization approach is used to separate the sensitive attributes and quasi attributes. The first round uses a novel approach called as enhanced semantic l-diversity technique to bucketize the tuples and also determine the correlation of the sensitive attributes to build different sensitive tables. The second round generates multiple quasi tables by performing slicing operation on concatenated correlated quasi attributes. It concatenate the attributes of the quasi tables with the ID's of the buckets from the different sensitive tables and perform random permutations on the buckets of quasi tables. Proposed model publishes the data with more privacy and high utility levels when compared to the existing models.
Cite This Paper
N.V.S. Lakshmipathi Raju, M.N. Seetaramanath, P. Srinivasa Rao, "A Novel Dynamic KCi - Slice Publishing Prototype for Retaining Privacy and Utility of Multiple Sensitive Attributes", International Journal of Information Technology and Computer Science(IJITCS), Vol.11, No.4, pp.18-32, 2019. DOI:10.5815/ijitcs.2019.04.03
Reference
[1]Anjum A, Ahmed Naveed, Malik S.U, Zubair S, Shahzad B, "An efficient approach for publishing microdata for multiple sensitive attributes", The Journal of Supercomputing, pp. 1- 29, 2018.
[2]Fung B. C, Wang K, Fu A. W. C, Philip S. Y, "Introduction to Privacy - Preserving Data Publishing: Concepts and techniques", Chapman and Hall/CRC Press, 2010.
[3]Fung B. C. M, Wang K., Chen R, Yu P. S, "Privacy-preserving data publishing: A survey of recent developments", ACM Computing Surveys, 42(4), article 14, 2010.
[4]Han J, Luo F, Lu J, Peng H, "SLOMS: A Privacy Preserving Data Publishing Method for Multiple Sensitive Attributes Micro data", Journal of Software 8(12), pp. 3096-3104, 2013.
[5]Hasan A. S. M, Jiang Q, Chen H, Wang S, "A New Approach to Privacy-Preserving Multiple Independent Data Publishing", Applied Sciences, 8, 783, pp. 1-22, 2018.
[6]Lakshmipathi Raju N.V.S, Seetaramanath M.N, Srinivasa Rao P,(in press) "An enhanced dynamic KC-Slice model for privacy preserving data publishing with multiple sensitive attributes by inducing sensitivity", Journal of King Saud University-Computer and Information Sciences, 2018.
[7]Liu F, Jia Y, Han W, "A new k – anonymity Algorithm towards multiple-sensitive attributes", In 2012 IEEE 12th International Conference on Computer and Information Technology (CIT) IEEE pp 768-772, 2012.
[8]Liu J, "Enhancing utility in privacy preserving data publishing" Doctoral dissertation, Applied Science: School of Computing Science, 2010.
[9]Liu J, Luo J, Huang J. Z, "Rating: privacy preservation for multiple attributes with different sensitivity requirements", In Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference IEEE, pp. 666-673, 2011.
[10]Li J, Wong R. C.W, Fu A.W.C, Pei J, "Achieving k-anonymity by clustering in attribute hierarchical structures", In International Conference on Data Warehousing and Knowledge Discovery. Springer, Berlin, Heidelberg. pp. 405-416, 2006.
[11]Li N, Li T, Suresh V, "t-closeness: Privacy beyond k-anonymity and l-diversity", In Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on. IEEE, pp. 106-115, 2007.
[12]Liu Q, Shen H, Sang Y, "Privacy-preserving data publishing for multiple numerical sensitive attributes", Tsinghua Science and Technology, 20(3), pp. 246-254, 2015.
[13]Li T, Li N, "On the tradeoff between privacy and utility in data publishing", In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ACM, pp 517 - 526, 2009.
[14]Maheshwarkar N, Pathak K, Choudhari N. S, "K-anonymity model for multiple sensitive attributes", International Journal of Computer Applications (IJCA), 2012.
[15]Mendes R, Vilela J. P, “Privacy-preserving data mining: methods, metrics, and applications", IEEE Access 5, pp. 10562-10582, 2017.
[16]Mohammed N, Fung B, Hung P. C, Lee, C. K, "Anonymizing healthcare data: a case study on the blood transfusion service", In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining ACM. pp. 1285-1294, 2009.
[17]Newman D, Hettich S, Blake C, Merz C, (2006) UCI repository of machine learning databases, university of california, irvine, Dept of Information and Computer Sciences,1998.
[18]Onashoga S.A, Bamiro S.A, Akinwale A.T, Oguntuase J.A, "KC-Slice: A dynamic privacy-preserving data publishing technique for multisensitive attributes", Information security journal: A Global Perspect 26(3), pp. 121-135, 2017.
[19]Ram Prasad Reddy S, VSVN Raju K, Valli Kumari V, "A novel approach for personalized privacy preserving data publishing with multiple sensitive attributes", International journal of engineering & technology 7(2.20) pp. 197-206, 2018.
[20]Susan V. S, Christopher, T, "Anatomization with slicing: a new privacy preservation approach for multiple sensitive attributes", Springer Plus, 5(1), 964, 2016.
[21]Usha P, Shriram R, Sathishkumar S, "Multiple sensitive attributes based privacy preserving data mining using k-anonymity", Int. J. Sci. Eng. Res, 5(4), 2014.
[22]Xiao X, Tao Y, "m-invariance: towards privacy preserving republication of dynamic datasets", In Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data ACM, pp. 689-700, 2007.
[23]Xu Y, Ma T, Tang M, Tian W, "A survey of privacy preserving data publishing using generalization and suppression. Applied Mathematics & Information Sciences, 8(3), 1103, 2014.
[24]Yuki Ina, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga, "Anonymization and analysis of horizontally and vertically divided user profile databases with multiple sensitive attributes", International conference on service operations and logistics and informatics(SOLI) pp.262-267, 2018.
[25]Emad Elabd, Hatem Abdulkader, Ahmed Mubark,"L-Diversity-Based Semantic Anonymization for Data Publishing", International Journal of Information Technology and Computer Science(IJITCS), vol. 7, no 10, pp. 1-7, 2015. DOI: 10.5815/ijitcs.2015.10.01.
[26]Ashoka K, Poornima B, "Enhanced Utility in Preserving Privacy for Multiple Heterogeneous Sensitive Attributes using Correlation an Personal Sensitivity flags", International conference on advances in computing, communications and informatics (ICACCI), pp. 970-976, 2017.
[27]Abou_el_ela Abdo Hussain, Nagy Ramadan Darwish, Hesham A. Henfy, "Multiple-Published Tables Privacy Preserving Data Mining: A Survey for Multiple- Published Tables Techniques", International Journal of Advanced Computer Science and Applications, vol.6, No.6, pp.80-85, 2015.
[28]Yifan Ye, Lixxia Wang, Jianmin Han, Sheng Qiu, Fangwei Luo, "An anonymization method combining anatomy and permutation for protecting privacy in micro data with multiple sensitive attributes", Proceedings of the 2017 International Conference on Machine Learning and Cybernetics, July 09-12, Ningbo, China, pp. 404-411, 2017.
[29]Kavitha D, "A Survey on Privacy Preserving Data Mining Techniques", International Journal of Computer & Mathematical Sciences(IJCMS), vol.7, issue 2, pp. 160-169, 2018.
[30]Aldeen Yousra S, Salleh Mazleena, "A new heuristic Anonymization Technique for Privacy Preserved Datasets Publication on Cloud Computing", Journal of Physics : Conf. Series, pp. 1-15, 2018.
[31]Ram Mohan Rao P, Murali Krishna S, Siva Kumar A.P, "Privacy Preservation techniques in big data analytics: a survey", Journal of Big Data, pp. 1-12, 2018.
[32]Sandeep Varma Nadimpalli, Valli Kumari Vatsavayi, "Verification in Privacy Preserving Data Publishing", Computing and Informatics vol. 35, pp. 1160-1188, 2016.