Call for Paper - November 2020 Edition
IJCA solicits original research papers for the November 2020 Edition. Last date of manuscript submission is October 20, 2020. Read More

A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Tasneem Jahan, Amit Saxena
10.5120/ijca2018917438

Tasneem Jahan and Amit Saxena. A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern. International Journal of Computer Applications 182(5):7-11, July 2018. BibTeX

@article{10.5120/ijca2018917438,
	author = {Tasneem Jahan and Amit Saxena},
	title = {A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2018},
	volume = {182},
	number = {5},
	month = {Jul},
	year = {2018},
	issn = {0975-8887},
	pages = {7-11},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume182/number5/29756-2018917438},
	doi = {10.5120/ijca2018917438},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Current scenario of large databases is in point of fact a major issue. Although, the conventional information examination seems to deal the extensive amounts of information. But the data analysts also attempt to analyze the productivity of data. This proposed work is an attempt to resolve the issue of digital information security by finding the highly frequent items in the dataset. Modified Frequent Web Access Pattern algorithm was developed in this work which find patterns in two scans. Technique called as super class substitution will be used here for perturbation of sensitive set of rules. It offers an added advantage of reducing the risk and the utility of database is also increased. Our experiment is carried out on a genuine dataset. The outcomes here, have shown that proposed work has better results over the previous methodologies.

References

  1. R..Agrawal and R..Srikant, “Fast Algorithms for Mining Association Rules in Large Databases,” Proc. 20th Int’l Conf. Very Large Data Bases, pp. 487-499, 1994.
  2. T. Calders and S. Verwer, “Three Naive Bayes Approaches for Discrimination-Free Classification,” Data Mining and Knowledge Discovery, vol. 21, no. 2, pp. 277-292, 2010.
  3. F. Kamiran and T. Calders, “Classification with no Discrimination by Preferential Sampling,” Proc. 19th Machine Learning Conf.Belgium and The Netherlands, pp 1-6, 2010.
  4. Huhtala, Y., Karkkainen, J., Porkka, P., and Toivonen, H., (1999), TANE: An Efficient Algorithm for discovering Functional and Approximate Dependencies, The Computer Journal, V.42, No.20, pp.100-107.
  5. Lichun Li, Rongxing Lu, Kim-Kwang Raymond Choo, Anwitaman Datta, and Jun Shao. “Privacy-Preserving-Outsourced Association Rule Mining on Vertically Partitioned Databases”. IEEE Transactions On Information Forensics And Security, Vol. 11, No. 8, August 2016 1847
  6. Shyue-liang Wang, Jenn-Shing Tsai and Been-Chian Chien, “Mining Approximate Dependencies Using Partitions on Similarity-relation-based Fuzzy Databases”, IEEE International Conference on Systems, Man and Cybernetics(SMC) 1999.
  7. Yao, H., Hamilton, H., and Butz, C., FD_Mine: Discovering Functional dependencies in a Database Using Equivalences, Canada, IEEE ICDM 2002.
  8. Wyss. C., Giannella, C., and Robertson, E. (2001), FastFDs: A Heuristic-Driven, Depth-First Algorithm for Mining Functional Dependencies from Relation Instances, Springer Berlin Heidelberg 2001.
  9. N. V. Muthu Lakshmi1 & K. Sandhya Rani, “Privacy Preserving Association Rule Mining in Vertically Partitioned Databases,” In IJCSA, vol. 39, no. 13, pp. 29-35, Feb. 2012.
  10. F. Giannotti, L. V. S.Lakshmanan, A. Monreale, D. Pedreschi, and H. Wang, “Privacy-Preserving Mining of Association Rules from Outsourced Transaction Databases,” IEEE Syst. J., vol. 7, no. 3, pp. 385- 395, Sep. 2013.
  11. J. Lai, Y. Li, R. H. Deng, J. Weng, C. Guan, and Q. Yan, “Towards Semantically Secure Outsourcing of Association Rule Mining on Categorical Data,” Inf. Sci.., vol. 267, pp. 267-286, May 2014.
  12. T. Tassa, “Secure Mining of Association Rules in Horizontally Distributed Databases Scalable Algorithms for Association Mining,” IEEE Trans.Knowl. Data Eng., vol. 26, no. 4, Apr. 2014.
  13. Thasneem M, S.Ramesh, Dr. T. Senthil Prakash. “An Effective Attack Analysis and Defense in Web Traffic Using Only Timing Information”. International Journal of Scientific Research & Engineering Trends Volume 3, Issue 3, May-2017, ISSN (Online): 2395-566X, www.ijsret.com
  14. L. Li, R. Lu, S. Member, K. R. Choo, and S. Member, “PrivacyPreserving-Outsourced Association Rule Mining on Vertically Partitioned Databases,” IEEE Trans. Info. Foren. Secur., vol. 11, no. 8, pp. 1847–1861, Aug. 2016.
  15. Jimmy Ming-Tai Wu, Justin Zhan, And Jerry Chun-Wei Lin. “Ant Colony System Sanitization Approach to Hiding Sensitive Itemsets”. Digital Object Identifier 10.1109/ACCESS.2017.2702281 June 28, 2017

Keywords

Data Mining, PPDM, MFWAP, Super class substitution, Data Perturbation,