Call for Paper - August 2022 Edition
IJCA solicits original research papers for the August 2022 Edition. Last date of manuscript submission is July 20, 2022. Read More

A Conceptual Framework for Hiding Sensitive Association Rules

Print
PDF
IJCA Proceedings on International Conference on Internet of Things, Next Generation Networks and Cloud Computing
© 2016 by IJCA Journal
ICINC 2016 - Number 1
Year of Publication: 2016
Authors:
Geeta S. Navale
Suresh N. Mali

Geeta S Navale and Suresh N Mali. Article: A Conceptual Framework for Hiding Sensitive Association Rules. IJCA Proceedings on International Conference on Internet of Things, Next Generation Networks and Cloud Computing ICINC 2016(1):18-21, July 2016. Full text available. BibTeX

@article{key:article,
	author = {Geeta S. Navale and Suresh N. Mali},
	title = {Article: A Conceptual Framework for Hiding Sensitive Association Rules},
	journal = {IJCA Proceedings on International Conference on Internet of Things, Next Generation Networks and Cloud Computing},
	year = {2016},
	volume = {ICINC 2016},
	number = {1},
	pages = {18-21},
	month = {July},
	note = {Full text available}
}

Abstract

Data mining process is used to extract knowledge from the database. Large numbers of data mining tools are available to get the useful information. These tools can be utilized to break the privacy and security of useful sensitive information present in the database. This sensitive information may be personal information, patterns, facts etc. This sensitive information if mined will result in loss of business logics of database owners. Hence there is a need to hide sensitive knowledge. The hiding process must ensure that the knowledgeshould be mined without disclosing sensitive association rules to the users with minimum impact on nonsensitive association rules. Also, intentional as well as unintentional attackers who are trying to retrieve sensitive association rules should not be successful once they are hidden. In this paper, the authorspropose a methodology to hide sensitive association rules.

References

  • Agrawal, R. , Imielinski, T. , Swami, "A. : Mining Association Rules between Sets of Items in Large Databases," ACM SIGMOD International Conference on Management of Data (SIGMOD'93), Washington D. C. , USA , pp. 207–216, May 1993
  • Geng L. , Hamilton, H. J. , "Interestingness Measures For Data Mining:A Survey," ACM Comput. Surv. (CSUR) 38(3), 9, 2006.
  • McGarry, K, "A survey of Interestingness Measures for Knowledge Discovery," Knowl. Eng. Rev. 20(1), pp. 39–61, 2005.
  • G. Dong and J. Li, "Interestingness of Discovered Association Rules in terms of Neighbourhood-Based Unexpectedness," Second Paci?c-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'98), pp. 72-86, 1998.
  • Lallich, S. , Teytaud, O. , Prudhomme, E. , "Association Rule Interestingness: Measure and Statistical Validation," Qual. Measur. Data Min. 43, pp. 251–276, 2006.
  • A. A. Freitas, "On Rule Interestingness Measures," Elsevier, Knowledge-Based Systems 12, pp. 309–315,1999.
  • Dasseni E, Verykios VS, Elmagarmid AK, Bertino E. , "Hiding Association Rules by Using Confidence and Support," In: Proceedings of the 4th International Workshop on Information Hiding, pp. 369–383, 2001.
  • Verykios VS, Pontikakis ED, Theodoridis Y, Chang L. , "Efficient Algorithms for Distortion and Blocking Techniques in Association Rule Hiding," Distributed Parallel Databases, pp. 22:85–104, 2007.
  • Wu Y-H, Chiang C-M, Chen ALP. , "Hiding Sensitive Association Rules with Limited Side Effects," IEEE Trans Knowledge Data Eng, pp. 19:29–42, 2007.
  • Gkoulalas-Divanis A, Verykios VS. , "Hiding Sensitive Knowledge Without Side Effects," Knowledge Inf Syst, pp. 20:263–299, 2009.
  • Gkoulalas-Divanis A, Verykios VS. , "Exact Knowledge Hiding Through Database Extension," IEEE Trans Knowledge Data Eng, pp. 21:699–713, 2009.