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

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Privacy Preserving Association Rule Hiding Techniques: Current Research Challenges

by Mohamed Refaat Abdellah, H. Aboelseoud M., Khalid Shafee Badran, M. Badr Senousy
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 6
Year of Publication: 2016
Authors: Mohamed Refaat Abdellah, H. Aboelseoud M., Khalid Shafee Badran, M. Badr Senousy
10.5120/ijca2016908446

Mohamed Refaat Abdellah, H. Aboelseoud M., Khalid Shafee Badran, M. Badr Senousy . Privacy Preserving Association Rule Hiding Techniques: Current Research Challenges. International Journal of Computer Applications. 136, 6 ( February 2016), 11-17. DOI=10.5120/ijca2016908446

@article{ 10.5120/ijca2016908446,
author = { Mohamed Refaat Abdellah, H. Aboelseoud M., Khalid Shafee Badran, M. Badr Senousy },
title = { Privacy Preserving Association Rule Hiding Techniques: Current Research Challenges },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 136 },
number = { 6 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume136/number6/24156-2016908446/ },
doi = { 10.5120/ijca2016908446 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:36:18.001438+05:30
%A Mohamed Refaat Abdellah
%A H. Aboelseoud M.
%A Khalid Shafee Badran
%A M. Badr Senousy
%T Privacy Preserving Association Rule Hiding Techniques: Current Research Challenges
%J International Journal of Computer Applications
%@ 0975-8887
%V 136
%N 6
%P 11-17
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Association rule mining is one of the most used techniques of data mining that are utilized to extract the association rules from large databases. Association rules are one of the most significant assets of any organization that can be used for business growth and profitability increase. It contains sensitive information that threatens the privacy of its publication and it should be hidden before publishing the database. Privacy preserving data mining (PPDM) techniques is used to preserve such confidential information or restrictive patterns from unauthorized access. The pattern can be represented in the form of a frequent itemset or association rule. Also, a rule or pattern is marked as sensitive if its disclosure risk is above a given threshold. This paper discusses the current techniques and challenges of privacy preserving in association rule mining. Also, presentation of metrics used to evaluate the performance of those approaches is also given. Finally, Interesting future trends in this research body are specified.

References
  1. Jajodia., C.F.a.S., The inference problem: A survey, in SIGKDD Exploration Newsletter. 2002. p. 6-11.
  2. M. Atallah, E.B., A. Elmagarmid, M. Ibrahim, V.S. Verykios, Disclosure limitation of sensitive rules, in Proceedings of the IEEE Knowledge and Data Engineering Exchange Workshop (KDEX’99). 1999. p. 45-52.
  3. E. Dasseni, V.S.V., A.K. Elmagarmid, E. Bertino. Association rule hiding. in Proceedings of the Fourth International Workshop on Information Hiding. 2001.
  4. Y. Saygm, V.S.V., C. Clifton, and Y. Saygin, Using unknowns to prevent discovery of association rules. ACM SIGMOD Record, 2001. Vol.(30)(No.4): p. 45-54.
  5. S.R.M. Oliveira, O.R.Z., Privacy preserving frequent itemset mining, in IEEE International Conference on Privacy, Security and Data Mining (CRPITS 2002). 2002. p. 43-54.
  6. E. Bertino, I.N.F., L.P. Povenza, A framework for evaluating privacy preserving data mining algorithms. Data Mining and Knowledge Discovery 2005. Vol.11 (No.2): p. 121-154.
  7. Jafari, S.-L.W.a.A., Hiding sensitive predictive association rules, in Systems, Man and Cybernetics, IEEE International Conference 2005. p. 164-169.
  8. Xingzhi Sun, Y., P.S., A border-based approach for hiding sensitive frequent itemsets, in Data Mining, Fifth IEEE International Conference. 2005. p. 1550-4786.
  9. Xingzhi Sun , Y., P.S., Hiding sensitive frequent itemsets by a border-based approach. Journal of Computing Science and Engineering, 2007. Vol.1(No.1): p. 74-94.
  10. Shyue-Liang Wang, D.P., Ayat Jafari Hiding collaborative recommendation association rules. Applied Intelligence, 2007. Vol.27(No.1): p. 67-77.
  11. T. Berberoglu and M. Kaya, Hiding Fuzzy Association Rules in Quantitative Data, in The 3rd InternationalConference on Grid and Pervasive Computing Workshops. 2008. p. 387-392.
  12. Chih-Chia Weng ; Dept. of Comput. Sci., N.D.U., Taoyuan ; Chen, Shan-Tai ; Hung-Che Lo, A Novel Algorithm for Completely Hiding Sensitive Association Rules, In Intelligent Systems Design and Applications, 2008. ISDA’08. Eighth International Conference. 2008. p. 202-208.
  13. C. N. Modi, U.P.R., and D. R. Patel, Maintaining privacy and data quality in privacy preserving association rule mining, In Computing Communication and Networking Technologies (ICCCNT), International Conference. 2010. p. 1-6.
  14. Yogendra Kumar Jain, V.K.Y., Geetika S. Panday, An Efficient Association Rule Hiding Algorithm for Privacy Preserving Data Mining. International Journal on Computer Science and Engineering (IJCSE), 2011. Vol.3: p. 2792 -2798.
  15. Komal Shah , A.T., Amit Ganatra Association Rule Hiding by Heuristic Approach to Reduce Side Effects & Hide Multiple RHS Items. International Journal of Computer Applications (0975 – 8887) 2012.
  16. D. Jain, P.K., R. Soni, and B. B. K. Chaurasia, Hiding Sensitive Association Rules without Altering the Support of Sensitive Item (s). Advances in Computer Science and Information Technology. Networks and Communications, 2012. Vol.3(No.2): p. 500–509.
  17. Rao, N.H.D.a.U.P., Hiding sensitive association rules to maintain privacy and data quality in database, in Advance Computing Conference (IACC), 2013 IEEE 3rd International. 2013. p. 1306–1310.
  18. Niteen Dhutraj, S.S., Vivek Kshirsagar Hiding Sensitive Association Rule For Privacy Preservation. IEEE Transactions on Knowledge and Data Engineering 2013.
  19. Peng Cheng, J.-S.P., Chun-Wei Lin, Use EMO to Protect Sensitive Knowledge in Association Rule Mining by Removing Items, in IEEE Congress on Evolutionary Computation (CEC). 2014: Beijing, China.
  20. T. Jahan, G.N.a.C.V.G.R. Data Perturbation and Features Selection in Preserving Privacy. In IEEE 2012 proceedings 9781-4673-1989-8/12. 2012.
  21. J. Liu, J.L.a.J.Z.H., Rating: Privacy Preservation for Multiple Attributes with Different Sensitivity requirements, In proceedings of 11th IEEE International Conference on Data Mining Workshops, IEEE 2011.
  22. A. Parmar, U.P.R., D. R. Patel, . Blocking based approach for classification Rule hiding to Preserve the Privacy in Database. In proceedings of International Symposium on Computer Science and Society, IEEE 2011.
  23. Animesh Tripathy , M.P. A novel framework for preserving privacy of data using correlation analysis. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI '12). ACM. 2012. NY, USA.
  24. S. Lohiya and L. Ragha. Privacy Preserving in Data Mining Using Hybrid Approach. In proceedings of Fourth International Conference on Computational Intelligence and Communication Networks, IEEE 2012.
  25. Luciano Bononi, M.B., Gabriele D'Angelo, Lorenzo Donatiello. Concurrent Replication of Parallel and Distributed Simulations. In Proceedings of the 19th Workshop on Principles of Advanced and Distributed Simulation (PADS '05). IEEE Computer Society. 2005. , Washington, DC, USA.
  26. G. V. Moustakides , V.S.V., A max-min approach for hiding frequent itemsets., In Workshops Proceedings of the 6th IEEE International Conference on Data Mining (ICDM2006). 2006. p. 502–506.
  27. S. Menon, S.S., and S. Mukherjee, Maximizing accuracy of shared databases when concealing sensitive patterns. Information Systems Research,16(3), 2005: p. 256–270.
  28. Dragos N. Trinca, Fast and Cost-Effective Algorithms for Information Extraction in some Computational Domains. 2008, University of Connecticut, Storrs: CT, USA. .
  29. Yongcheng Luo, Y.Z.a.J.L. A Survey on the Privacy Preserving Algorithm of Association Rule Mining. In Proceedings of the IEEE Second International Symposium on Electronic Commerce and Security (ISECS '09). 2009. Washington, DC, USA, 241245.
  30. T. Sirole, J.C., A Survey of Various Methodologies for Hiding Sensitive Association Rules. International Journal of Computer Applications (0975 – 8887), 2014. Vol. 96(No.18).
Index Terms

Computer Science
Information Sciences

Keywords

Privacy preserving data mining Association Rule Hiding Approaches.

Powered by PhDFocusTM