The week's pick
Random Articles
Reseach Article
Review on Privacy Preservation by Applying Scalable MapReduce BottomUp Generalization (MRBUG) Technique
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 139 - Number 6 |
| Year of Publication: 2016 |
| Authors: Umar Y. Solanki, Rubeena A. Khan |
10.5120/ijca2016908432
|
Umar Y. Solanki, Rubeena A. Khan . Review on Privacy Preservation by Applying Scalable MapReduce BottomUp Generalization (MRBUG) Technique. International Journal of Computer Applications. 139, 6 ( April 2016), 17-21. DOI=10.5120/ijca2016908432
Abstract
Privacy is one of the most concerned issues in data publishing. Personal data like electronic health records and financial transaction records are usually deemed extremely sensitive although these data can offer significant human benefits if they are analyzed and mined by organizations such as disease research Centre. The emerging research field in data mining, Privacy Preserving Data Publishing (PPDP) [11], targets these challenges. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. It aims at developing techniques that enable publishing data while minimizing data distortion for maintaining utility and ensuring that privacy is preserved.
References
- latanya sweeney,” k-anonymity- a model for protecting privacy”, international journal of uncertainty, puzziness and knowledge-Based Systems Vol. 10, No. 5 (2002) 557-570
- Benjamin C. M. Fung ,Ke Wang, Yu, “Top-Down Specialization for Information and Privacy Preservation”
- Ke Wang, Yu, P.S,Chakraborty, S, “ Bottom-up generalization: a data mining solution to privacy protection”.
- B.C.M. Fung, K. Wang, R. Chen and P.S. Yu, “Privacy-Preserving Data Publishing: A Survey of Recent Developments,” ACMComput. Surv.,vol. 42, no. 4, pp.1-53, 2010.
- P.Usha R.Shriram S.Sathishkumar,” Sensitive Attribute based Non-HomogeneousAnonymization for Privacy Preserving Data Mining”, ISBN No.978-1-4799-3834-6/14/$31.00©2014 IEEE
- X. Xiao and Y. Tao, “Anatomy: Simple and Effective Privacy Preservation,” Proc. 32nd Int’l Conf. Very Large Data Bases(VLDB’06), pp. 139-150, 2006.
- Syst., vol. 33, no. 3, pp. 1-47, 2008.B. Fung, K. Wang, L. Wang and P.C.K. Hung, “Privacy- Preserving Data Publishing for Cluster Analysis,” Data Knowl.Eng., Vol.68,no.6, pp.552-575, 2009.
- B.C.M. Fung, K. Wang, and P.S. Yu, “Anonymizing Classification Data for Privacy Preservation,” IEEE Trans. Knowledge and Data Eng., vol.19, no. 5, pp. 711-725, May 2007.
- Hassan Takabi, James B.D. Joshi and Gail-Joon Ahn, “Security and Privacy Challenges in Cloud Computing Environments”.
- J. Dean and S. Ghemawat, “Mapreduce: Simplified Data Processing on Large Clusters,” Comm. ACM, vol. 51, no. 1, pp. 107-113,2008.
- Dean J, Ghemawat S. “Mapreduce: a flexible data processing tool,” Communications of the ACM 2010;53(1):72–77.DOI:10.1145/1629175.1629198.
- Lei Xu, Chunxiao Jiang, (member, ieee), Jian Wang, (member, ieee),Jian Yuan, (member, ieee), and Yong Ren, (member, ieee),” information security in big data:
- Privacy and Data Mining” , 10.1109/ACCESS.2014.2362522L. Sweeney. Datafly: A system for providing anonymityin medical data. In International Conference on DatabaseSecurity, pages 356–381, 1998.
Index Terms
Keywords