A Hybrid K-anonymity Data Relocation Technique for Privacy Preserved Data Mining in Cloud Computing = A Hybrid K-anonymity Data Relocation Technique for Privacy Preserved Data Mining in Cloud Computing

다국어 초록 (Multilingual Abstract)

The unprecedented power of cloud computing (CC) that enables free sharing of confidential data records for further analysis and mining has prompted various security threats. Thus, supreme cyberspace security and mitigation against adversaries attack during data mining became inevitable. So, privacy preserving data mining is emerged as a precise and efficient solution, where various algorithms are developed to anonymize the data to be mined. Despite the wide use of generalized K-anonymizing approach its protection and truthfulness potency remains limited to tiny output space with unacceptable utility loss. By combining L-diversity and (α,k)-anonymity, we proposed a hybrid K-anonymity data relocation algorithm to surmount such limitation. The data relocation being a tradeoff between trustfulness and utility acted as a control input parameter. The performance of each K-anonymity`s iteration is measured for data relocation. Data rows are changed into small groups of indistinguishable tuples to create anonymizations of finer granularity with assured privacy standard. Experimental results demonstrated considerable utility enhancement for relatively small number of group relocations.

참고문헌 (Reference)

1 M. E. Nergiz, "δ-presence without complete world knowledge" 22 (22): 868-883, 2010

2 S. Kumara, "Virtualization, The Great Thing and Issues in Cloud Computing" 338-341, 2013

3 S. Moro, "Using Data Mining for Bank Direct Marketing: An application of the CRISP-DM methodology" 2011

4 M. E. Nergiz, "Thoughts on k-anonymization" 63 (63): 622-645, 2007

5 G. Ateniese, "Scalable and efficient provable data possession" 2008

6 Y. Pan, "Research on privacy preserving on K-anonymity" 7 (7): 1649-1656, 2012

7 X. Yang, "Recent Research Advances in e-Science" 12 (12): 353-356, 2009

8 P. Samarati, "Protecting respondents’ identities in microdata release" 13 (13): 1010-1027, 2001

9 M. E. Nergiz, "Preservation of utility through hybrid k-anonymization" 8058 : 97-111, 2013

10 M. E. Nergiz, "Preservation of utility through hybrid k-anonymization" Springer Berlin Heidelberg 97-111, 2013

11 C. Kim, "Performance Analysis of Top-K High Utility Pattern Mining Methods" 16 (16): 89-95, 2015

12 K. Lefevre, "Mondrian Multidimensional K-Anonymity" 2006

13 A. Machanavajjhala, "L-Diversity" 1 (1): 3-, 2007

14 K. Lefevre, "Incognito : Efficient Full-Domain K-Anonymity" 2005

15 L. Wang, "In cloud, can scientific communities benefit from the economies of scale?" 23 (23): 296-303, 2012

16 M. E. Nergiz, "Hybrid k-Anonymity" 44 : 51-63, 2014

17 M. E. Nergiz, "Hiding the presence of individuals from shared databases" 2007

18 B. Hore, "Flexible Anonymization For Privacy Preserving Data Publishing : A Systematic Search Based Approach" SDM

19 G. Ghinita, "Fast data anonymization with low information loss" 2007

20 R. J. Bayardo, "Data privacy through optimal k-anonymization" 2005

21 R. Buyya, "Cloud computing and emerging IT platforms:Vision, hype, and reality for delivering computing as the 5th utility" 25 : 599-616, 2009

22 H. A. Elsalamony, "Bank Direct Marketing Analysis of Data Mining Techniques" 12-22, 2014

23 E. T. Wang, "An efficient sanitization algorithm for balancing information privacy and knowledge discovery in association patterns mining" 65 (65): 463-484, 2008

24 D. Zissis, "Addressing cloud computing security issues" 28 (28): 583-592, 2012

25 J. J. Panackal, "Adaptive Utility-based Anonymization Model: Performance Evaluation on Big Data Sets" 50 : 347-352, 2015

26 G. Aggarwal, "Achieving anonymity via clustering" 2006

27 X. Dong, "Achieving an effective, scalable and privacy-preserving data sharing service in cloud computing" 151-164, 2014

28 W. Cohen, "Absorptive capacity: a new perspective on learning and innovation" 128-152, 1990

29 J. L. Lin, "A hybrid method for k-anonymization" 2008

30 X. Zhang, "A hybrid approach for scalable sub-tree anonymization over big data using MapReduce on cloud" 80 (80): 1008-1020, 2014

31 S. Moro, "A data-driven approach to predict the success of bank telemarketing" 62 : 22-31, 2014

32 R. C. Wong, "(α,k)-Anonymity : An Enhanced k -Anonymity Model for Privacy-Preserving Data Publishing" 2006