The week's pick
Random Articles
Reseach Article
Implementation of Multi-Level Trust in Privacy Preserving Data Mining against Non-Linear Attack
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 115 - Number 21 |
| Year of Publication: 2015 |
| Authors: Vaishali Bhorde, R.n.phursule |
10.5120/20272-2685
|
Vaishali Bhorde, R.n.phursule . Implementation of Multi-Level Trust in Privacy Preserving Data Mining against Non-Linear Attack. International Journal of Computer Applications. 115, 21 ( April 2015), 1-6. DOI=10.5120/20272-2685
Abstract
The study of perturbation based Privacy Preserving Data Mining (PPDM) [1] [2] approaches introduces random perturbation that is number of changes made in the original data. The limitation of existing work is single level trust on data miners but proposed work is focus on perturbation based PPDM to multilevel trust. [1] When data owner sends number of perturbated copy to the trusted third party, adversary cannot find the original copy from the perturbated copy means the adversary diverse from original copy this is known as the diversity attack. To prevent diversity attack is main goal of Multilevel Trust in Privacy Preserving Data Mining (MLT-PPDM) services. [1]The different MLT-PPDM algorithms are used to produce noise into original data. In existing system by applying nonlinear collusion attack on MLT-PPDM approach, it is possible to reconstruct original data. In proposed system by applying masking noise linear transformation algorithm which produce noise into original data. When same nonlinear collusion attack is applied on proposed approach it cannot reconstruct original data means it preserve the privacy. That means existing system is limited only for linear attack. [1] But proposed system is working on the non-linear attack also. Linear attack is calculating average between all perturbated copies. Nonlinear attack is calculating minimum, maximum, median function estimation.
References
- Yaping Li, Minghua Chen, Qiwei Li, and Wei Zhang, "Enabling Multilevel Trust in Privacy Preserving Data Mining", IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO. 9, SEPTEMBER 2012.
- R. Agrawal and R Srikant, "Privacy Preserving Data Mining,"Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '00), 2000.
- K. Chen and L. Liu, "Privacy Preserving Data Classification with Rotation Perturbation," Proc. IEEE Fifth Int'l Conf. Data Mining, 2005.
- Z. Huang, W. Du, and B. Chen, "Deriving Private Information From Randomized Data," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD), 2005.
- F. Li, J. Sun, S. Papadimitriou, G. Mihaila, and I. Stanoi, "Hiding in the Crowd: Privacy Preservation on Evolving Streams Through Correlation Tracking," Proc. IEEE 23rd Int'l Conf. Data Eng. (ICDE), 2007.
- K. Liu, H. Kargupta, and J. Ryan, "Random Projection-Based Multiplicative Data Perturbation for Privacy Preserving Distributed Data Mining," IEEE Trans. Knowledge and Data Eng. , vol. 18,no. 1, pp. 92-106, Jan. 2006.
- J. Vaidya and C. Clifton,"Privacy-Preserving K-Means Clustering over Vertically Partitioned Data," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, 2003.
- Kanishka Bhaduri, Mark D. Stefanski and Ashok N. Srivastava, "Privacy-Preserving Outlier Detection Through Random Nonlinear Data Distortion" IEEE TRANSACTIONS ON SYSTEMS VOL. 41, NO. 1, 2011
- Benjamin C. M. Fung, Ke Wang, Philip S. Yu "Anonymizing Classification Data forPrivacy Preservation" IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 19, NO. 5, MAY 2007
- B. C. M. Fung, K. Wang, R. Chen, and P. S. Yu. Privacy
- Xiao-Bai Li, Sumit Sarkar "A Tree-Based Data Perturbation Approach for Privacy Preserving Data Mining" 19 July 2006.
- J. Vaidya and C. W. Clifton, "Privacy Preserving Association Rule Mining in Vertically Partitioned Data," Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, 2002.
- O. Goldreich, "Secure Multi-Party Computation," Final (incomplete) draft, version 1. 4, 2002.
Index Terms
Keywords