CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

An Optimization based Modified Maximum Sensitive Item-Sets Conflict First Algorithm (MSICF) for Hiding Sensitive Item-Sets

by D. Jaya Kumari, Nistala. V. E. S.murthy, S. Srinivasa Suresh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 4
Year of Publication: 2013
Authors: D. Jaya Kumari, Nistala. V. E. S.murthy, S. Srinivasa Suresh
10.5120/12479-8881

D. Jaya Kumari, Nistala. V. E. S.murthy, S. Srinivasa Suresh . An Optimization based Modified Maximum Sensitive Item-Sets Conflict First Algorithm (MSICF) for Hiding Sensitive Item-Sets. International Journal of Computer Applications. 72, 4 ( June 2013), 1-4. DOI=10.5120/12479-8881

@article{ 10.5120/12479-8881,
author = { D. Jaya Kumari, Nistala. V. E. S.murthy, S. Srinivasa Suresh },
title = { An Optimization based Modified Maximum Sensitive Item-Sets Conflict First Algorithm (MSICF) for Hiding Sensitive Item-Sets },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 4 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number4/12479-8881/ },
doi = { 10.5120/12479-8881 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:37:00.430435+05:30
%A D. Jaya Kumari
%A Nistala. V. E. S.murthy
%A S. Srinivasa Suresh
%T An Optimization based Modified Maximum Sensitive Item-Sets Conflict First Algorithm (MSICF) for Hiding Sensitive Item-Sets
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 4
%P 1-4
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In privacy preserving data mining, utility mining plays an important role. In privacy preserving utility mining, some sensitive itemsets are hidden from the database according to certain privacy policies. Hiding sensitive itemsets from the adversaries is becoming an important issue nowadays. Also, only very few methods are available in the literature to hide the sensitive itemsets in the database. The existing paper utilized two algorithms; such as HHUIF and MSICF are conceal the sensitive itemsets, so that the adversaries cannot mine them from the modified database. To accomplish the hiding process, this method finds the sensitive itemsets and modifies the frequency of the high valued utility items. But, the performance of this method lacks if the utility value of the items are same. To solve this problem, in this paper a modified MSICF algorithm with Item Selector (MMIS) is proposed. The MMIS algorithm computes the sensitive itemsets by utilizing the user defined utility threshold value. The proposed MMIS reduces the computation complexity as well as improves the hiding performance of the itemsets. The algorithm is implemented and the resultant itemsets are compared against the itemsets that are obtained from the conventional privacy preserving utility mining algorithms.

References
  1. Benjamin C. M. Fung, Ke Wang, Rui Chen And Philip S. Yu, "Privacy-Preserving Data Publishing: A Survey of Recent Developments", ACM Computing Surveys, Vol. 42, No. 4, Article 14, pp. 1-53, June 2010.
  2. Hillol Kargupta, Souptik Datta, Qi wang and Krishnamoorthy Sivakumar, "Random Data Perturbation Techniques and A Privacy Preserving Data Mining", in proceedings of IEEE International Conference on Data Mining, pp. 1-23, 2003
  3. Michal Sramka, "Data Mining as a tool in privacy preserving data publishing", Tatra M t. Math. Publications, Vol. 45, pp. 151–159, 2010
  4. Md. Riyazuddin, Dr. V. V. S. S. S. Balaram, Md. Afroze, Md. JaffarSadiq and M. D. Zuber, "An Empirical Study on Privacy Preserving Data Mining", International Journal of Engineering Trends and Technology, Vol. 3, No. 6, pp. 687-693, 2012
  5. Xiaodan Wu, Chao-HsienChu, Yunfeng Wang, Fengli Liu and Dianmin Yue, "Privacy preserving data mining research: current status and key issues", Springer -Lncs, pp. 762-772, 2007
  6. Elisa Bertino, Igor Nai Fovino and Loredana Parasiliti Provenza, "A Framework for Evaluating Privacy Preserving Data Mining Algorithms", Data Mining and Knowledge Discovery, Vol. 11, pp. 121–154, 2005
  7. C. Clifton, "Using Sample Size to Limit Exposure to Data Mining", Journal of Computer Security, Vol. 8, pp. 281- 307, Dec. 2000.
  8. Y. Saygin, V. S. Verykios, C. Clifton, "Using Unknowns to Prevent Discovery of Association Rules", SIGMOD Record, Vol. 30, pp. 45- 54, Dec. 2001
  9. A. Evfimievski, "Randomization in Privacy Preserving Data Mining", in Proceedings of the SIGKDD Explorations, Vol. 4, pp. 43- 48, Dec. 2002
  10. M. Kantarcioglu, C. Clifton, "Privacy-preserving distributed mining of association rules on horizontally partitioned data", ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Jun. 2002
  11. Mohammad Naderi Dehkordi, Kambiz Badie and Ahmad Khadem Zadeh, "A Novel Method for Privacy Preserving in Association Rule Mining Based on Genetic Algorithms", Journal of Software, Vol. 4, No. 6, pp. 555-562, August 2009
  12. M. Sathiya Prabha and S. Vijayarani, "Association Rule Hiding using Artificial Bee Colony Algorithm", International Journal of Computer Applications, Vol. 33, No. 2, pp. 41-47, November 2011
  13. Nissim Matatov, Lior Rokach and Oded Maimon, "Privacy preserving data mining: A feature set partitioning approach", Information Sciences, Vol. 180, pp. 2696–2720, 2010
  14. Ziauddin, Shahid Kammal, Khaiuz Zaman Khan, Muhammad Ijaz Khan, "Research on Association Rule Mining", Advances in Computational Mathematics and its Applications (ACMA), Vol. 2, No. 1, pp. 226-236, 2012
  15. Guillermo Navarro-Arribas, Vicenç Torra, Arnau Erola and Jordi Castellà-Roca, "User k-anonymity for privacy preserving data mining of query logs", Information Processing and Management 48, pp. 476–487,2012
  16. Keke Chen and Ling Liu, "Privacy preserving Multiparty Collaborative Minin with Geometric Data Perturbation", IEEE Transactions on Parallel and Distributed Computing, Vol. Xx, No. Xx, pp. 1-13, January 2009
  17. Jieh-Shan Yeh and Po-Chiang Hsu, "HHUIF and MSICF: Novel algorithms for privacy preserving utility mining", Expert Systems with Applications, Vol. 37, pp. 4779–4786, 2010
  18. Stanley R. M. Oliveira and Osmar R. Zaiane, "Privacy Preserving Frequent Itemset Mining", In Proceedings of the IEEE international conference on Privacy, security and data mining, Vol. 14, pp. 43-54, 2002
Index Terms

Computer Science
Information Sciences

Keywords

Utility Mining Privacy Preserving Utility Mining Sensitive Itemsets Utility Value Frequency Value Maximum Sensitive Itemsets Conflict First (MSICF)

Powered by PhDFocusTM