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Reseach Article

C Privacy Prevention of Discriminating Rules by Perturbing Sensitive Items

by Deepak Patel, Vineet Richhariya
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 2 - Number 8
Year of Publication: 2015
Authors: Deepak Patel, Vineet Richhariya
10.5120/cae2015651825

Deepak Patel, Vineet Richhariya . C Privacy Prevention of Discriminating Rules by Perturbing Sensitive Items. Communications on Applied Electronics. 2, 8 ( September 2015), 12-16. DOI=10.5120/cae2015651825

@article{ 10.5120/cae2015651825,
author = { Deepak Patel, Vineet Richhariya },
title = { C Privacy Prevention of Discriminating Rules by Perturbing Sensitive Items },
journal = { Communications on Applied Electronics },
issue_date = { September 2015 },
volume = { 2 },
number = { 8 },
month = { September },
year = { 2015 },
issn = { 2394-4714 },
pages = { 12-16 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume2/number8/416-2015651825/ },
doi = { 10.5120/cae2015651825 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:40:22.781528+05:30
%A Deepak Patel
%A Vineet Richhariya
%T C Privacy Prevention of Discriminating Rules by Perturbing Sensitive Items
%J Communications on Applied Electronics
%@ 2394-4714
%V 2
%N 8
%P 12-16
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the increase of digital data on servers different approach of data mining is done. This lead to important issue of proving privacy to the unfair information against any person, place, community etc. So Privacy preserving mining come in existence. This paper provide privacy for sensitive rule that discriminate data on the basis of community, gender, country, etc. So finding of those rules and suppression is done. Perturbation technique is use for the hiding sensitive rules. Experiment is done on real adult dataset for different ratio. Results shows that proposed work is better in maintaining the originality, reduce execution time, reduce data loss, at last suppress rules while other rules are remain unaffected.

References
  1. Giannotti, Laks V. S. Lakshmanan, Anna Monreale, Dino Pedreschi, and Hui (Wendy) Wang, In IEEE Systems Journal, VOL. 7, NO. 3, SEPTEMBER 2013, pp. 385-395. “Privacy-Preserving Mining of Association Rules from Outsourced Transaction Databases”
  2. C. Tai, P. S. Yu, and M. Chen, in Proc. Int. Knowledge Discovery Data Mining, 2010, pp. 473–482. “K-support anonymity based on pseudo taxonomy for outsourcing of frequent itemset mining,”
  3. W.K. Wong, D. W. Cheung, E. Hung, B. Kao, and N. Mamoulis, in Proc. Int. Conf. Very Large Data Bases, 2007, pp. 111–122. “Security in outsourcing of association rule mining,”
  4. K.Sathiyapriya and Dr. G.Sudha Sadasivam, In IJKDP Vol.3 No 2– March-2013, pp 119-131. “ A Survey on Privacy Preserving Association Rule Mining”
  5. R. Agrawal and R. Srikant, in Proc.ACM SIGMOD Int. Conf. Manage. Data, 2000, pp. 439–450. “Privacy-preserving data mining,”
  6. M.Mahendran, 2Dr.R.Sugumar International Journal of Advanced Research in Computer and Communication Engineering. Vol. 1, Issue 9, November 2012. “An Efficient Algorithm for Privacy Preserving Data Mining Using Heuristic Approach”
  7. Z. Yang and R. N. Wright. In IEEE Trans. on Knowledge and Data Engineering , 2006, pp.1253–1264. “Privacy-preserving computation of bayesian networks on vertically partitioned data.”
  8. Yaping Li, Minghua Chen, Qiwei Li, and Wei Zhang. IEEE transaction on knowledge data engineering, VOL. 24, NO. 9, SEPTEMBER 2012. “Enabling Multilevel Trust in Privacy Preserving Data Mining”
  9. Sara Hajian and Josep Domingo-Ferrer. “A Methodology for Direct and Indirect Discrimination Prevention in Data Mining”. IEEE transaction on knowledge data engineering, VOL. 25, NO. 7, JULY 2013.
  10. Mohamed R. Fouad, Khaled Elbassioni, and Elisa Bertino. IEEE transaction on knowledge data engineering VOL. 26, NO. 7, JULY 2014. A Supermodularity-Based Differential “Privacy Preserving Algorithm for Data Anonymization”.
  11. F. Kamiran, T. Calders, and M. Pechenizkiy, Proc. IEEE Int’l Conf. Data Mining (ICDM ’10), pp. 869-874, 2010. “Discrimination Aware Decision Tree Learning,”
  12. D. Pedreschi, S. Ruggieri, and F. Turini, Proc. 14th ACM Int’l Conf. Knowledge Discovery and Data Mining (KDD ’08), pp. 560-568, 2008. “Discrimination-Aware Data Mining,”
  13. D. Pedreschi, S. Ruggieri, and F. Turini, Proc. Ninth SIAMData Mining Conf. (SDM ’09), pp. 581-592, 2009. “Measuring Discrimination in Socially-Sensitive Decision Records,”
  14. Yogachandran Rahulamathavan, Raphael C.-W. Phan, Suresh Veluru, Kanapathippillai Cumanan and Muttukrishnan Rajarajan IEEE IEEE transaction on dependable and secure computing, VOL. 11, NO. 5, September 2014. .“Privacy-Preserving Multi-Class Support Vector Machine for Outsourcing the Data Classification in Cloud “.
  15. R. Kohavi and B. Becker, “http://archive.ics.uci.edu/ml/ datasets /Adult, 1996. UCI Repository of Machine Learning Databases,”.
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Computer Science
Information Sciences

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