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

Association Rules Hiding for Privacy Preserving Data Mining: A Survey

by Gehad Ahmed Sultan Abd El_Aleem, Laila Abd_Ellatif, Ahmed Sharaf
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 150 - Number 12
Year of Publication: 2016
Authors: Gehad Ahmed Sultan Abd El_Aleem, Laila Abd_Ellatif, Ahmed Sharaf
10.5120/ijca2016911664

Gehad Ahmed Sultan Abd El_Aleem, Laila Abd_Ellatif, Ahmed Sharaf . Association Rules Hiding for Privacy Preserving Data Mining: A Survey. International Journal of Computer Applications. 150, 12 ( Sep 2016), 34-43. DOI=10.5120/ijca2016911664

@article{ 10.5120/ijca2016911664,
author = { Gehad Ahmed Sultan Abd El_Aleem, Laila Abd_Ellatif, Ahmed Sharaf },
title = { Association Rules Hiding for Privacy Preserving Data Mining: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 150 },
number = { 12 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 34-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume150/number12/26149-2016911664/ },
doi = { 10.5120/ijca2016911664 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:55:50.106224+05:30
%A Gehad Ahmed Sultan Abd El_Aleem
%A Laila Abd_Ellatif
%A Ahmed Sharaf
%T Association Rules Hiding for Privacy Preserving Data Mining: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 150
%N 12
%P 34-43
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

(PPDM) privacy preserving data mining is recent advanced research in (DM) data mining field; Many efficient and practical techniques have been proposed for hiding sensitive patterns or information from been discovered by (DM) data mining algorithms. (ARM) Association rule mining is the most important tool in (DM) data mining, that is considered a powerful and interested tool for discovering relationships between items, which are hidden in large database and may provide business competitors with an advantage, thus the hiding of association rules is the most important point in (PPDM) privacy preserving data mining for protecting sensitive and crucial data against unauthorized access; Many Practical techniques and approaches have been proposed for hiding association rules for (PPDM) privacy preserving data mining; In this paper the current existing techniques and algorithms for all approaches for (ARH) association rule hiding have been summarized.

References
  1. B. Davies, D. BANisAR Global Trends in Privacy Protection: An International Survey of Privacy, Data Protection, and Surveillance Laws and Developments (August 30, 2012). John Marshall Journal of Computer & Information Law, Vol. XVIII, No. 1, Fall 1999.
  2. F. Berlin Germany International Conference of the PRESCIENT Project 27 – 28 November 2012.
  3. D. O’Leary, Knowledge Discovery as a Threat to Database Security, in: G. Piatetsky-Shapiro, W.J. Frawley (Eds.), Knowledge Discovery in Databases, AAAI/MIT Press, Cambridge, 1991, pp. 507–516.
  4. C. Clifton, D. Marks, Security and privacy implications of data mining, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, 1996, pp. 15–19.
  5. R. Agrawal, R. Srikant “Privacy Preserving Data Mining,” Proc. 2000 ACM SIGMOD Int’l Conf. Management of Data, ACM Press, 2000, pp. 439-450.
  6. A. Evfimievski, J. Gehrke, and R. Srikant “Limiting Privacy Breaches in Privacy Preserving Data Mining,” Proc. 22nd ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems, ACM Press, 2003, pp. 211-222.
  7. V. Verykios, E. Bertino, I. Fovino, L. Provenza, Y. Saygin, and Y. Theodoridis, State-of-the-art in privacy preserving data mining, ACM SIGMOD Record 33 (1) (2004) 50 – 57.
  8. W. Bart Schermer, The limits of privacy in automated profiling and data mining, computer law & security review 27 (2011) 45 – 52.
  9. R. Agrawal, M. Kaufmann“Hippocratic Databases,” Proc. 28th Int’l Conf. Very Large Databases, 2002, pp. 143-154.
  10. Y. Lindell, B. Pinkas “Privacy Preserving Data Mining,” Proc. Crypto 2000, Springer Verlag, 2000, pp. 37-55.
  11. R. Agrawal, R. Srikant “Information Sharing across Private Databases,” Proc. 2003 ACM SIGMOD Int’l Conf. Management of Data, ACM Press, 2003, pp. 86-97.
  12. J. Dyer “Building the IBM 4758 Secure Coprocessor,” Computer, Oct. 2001, pp. 57-66.
  13. M. Bawa, R. Bayardo, and R. Agrawal “Privacy Preserving Indexing of Documents on the Network,” to appear in Proc. 29th Int’l Conf. Very Large Databases, Morgan Kaufmann, 2003.
  14. B. Chor “Private Information Retrieval,” IEEE Symp. Foundations of Computer Science, IEEE CS Press, 1995, pp. 41-50.
  15. G. Navarro-Arribas, V. Torra “Information fusion in data privacy: A survey” Information Fusion 13 (2012) 235–244.
  16. W. Xiaodan, C. ChaoHsien, W. Yunfeng, L. Fengli, Y. Dianmin ”Privacy Preserving Data Mining Research: Current Status and Key Issues” Computational Science – ICCS, Volume 4489, 2007, pp 762.
  17. J. Han, M. Kamber Data Mining: Concepts and Techniques University of Illinois at Urbana-Champaign / 2006 / pp5.
  18. H. Margaret Dunham Data Mining Introductory and Advanced Topics / 2003 / pp.9-10.
  19. Hand, Mannila, and Smyth Principles of Data Mining / 2001 /pp1.
  20. M. Berry, G. Linoff Data Mining Techniques / Paperback / 2004 /pp2.
  21. J. Yeh, P. Hsu” HHUIF and MSICF: Novel algorithms for privacy preserving utility mining” Expert Systems with Applications 37 (2010) 4779–4786
  22. E. Bertino, I. Nai Fovino” A Framework for Evaluating Privacy Preserving Data Mining Algorithms” Data Mining and Knowledge Discovery, 11, 121–154, 2005
  23. F. Bonchi, B. Malin, Y. Saygin” Recent advances in preserving privacy when mining data” Data & Knowledge Engineering 65 (2008) 1–4.
  24. X. Qi, M. Zong “An Overview of Privacy Preserving Data Mining” Procedia Environmental Sciences 12 ( 2012 ) 1341 – 1347.
  25. R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases”. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, May 26-28 1993, pp. 207-216.
  26. R. Agarwal, R. Srikant, “Fast algorithm for mining association rules in large databases”, Proceedings of 1994 International Conference on VLDB, 1994, pp. 487-499.
  27. J. Han, J. Pei, Y. Yin, R. Mao,” Mining Frequent Patterns without Candidate Generation: A Frequent- Pattern Tree Approach”, Data Mining and Knowledge Discovery, 8, 2004, pp. 53-87.
  28. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A.I. Verkamo, “Fast Discovery of Association Rules,” Advances in Knowledge Discovery and Data Mining, chapter 12, U.M. Fayyad et al., eds., AAAI/MIT Press, pp. 307-328, 1996.
  29. J. Liu, Y. Pan, K. Wang, and J. Han, “Mining Frequent Item Sets by Opportunistic Projection,” Proc. ACM Conf. Knowledge Discovery and Data Mining, 2002.
  30. S.J. Yen and A.L.P. Chen, “A Graph-Based Approach for Discovering Various Types of Association Rules,” IEEE Trans. Knowledge and Data Eng.,vol. 13, no. 5, 2001.
  31. M.J. Zaki, “Scalable Algorithms for Association Mining,” IEEE Trans. Knowledge and Data Eng.,vol. 12, no. 3, pp. 372-390, 2000.
  32. D. Dudek,” RMAIN: Association rules maintenance without reruns through data”, Information Sciences 179 (2009) 4123–4139.
  33. L. Guang-yuan, C. Dan-yang, G. Jian-wei” Association Rules Mining with Multiple Constraints” Procedia Engineering 15 (2011) 1678 – 1683
  34. N. Jain,V. Sharma,M. Malviya,” Reduction of Negative and Positive Association Rule Mining and Maintain Superiority of Rule Using Modified Genetic Algorithm” International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume-2 Number-4 Issue-6 December-2012.
  35. C. Chiang, A. Chen Hiding Sensitive Association Rules with Limited Side Effects. IEEE Transactions on Knowledge and Data Engineering, 19(1), 2007.
  36. K. Shah, A. Thakkar, A. Ganatra “A Study on Association Rule Hiding Approaches”International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-1, Issue-3, Feb 2012.
  37. E. Dasseni, V. S.Verykios, A. K.Elmagarmid, and E. Bertino, “Hiding Association Rules by using Confidence and Support,” In Proceedings of the 4th Information Hiding Workshop (2001), pp.369– 383.
  38. S. R. M. Oliveira, O. R. Zaïane, “Privacy Preserving Frequent Itemset Mining”, IEEE International Conference on Data Mining Workshop on Privacy, Security, and Data Mining, Maebashi City, Japan. Conferences in Research and Practice in Information Technology, Vol. 14.2002.
  39. S. R. M. Oliveira, O. R. Zaïane,”Protecting sensitive knowledge by data sanitization”, In: Proc. of the 3rd IEEE Int’l Conf. on Data Mining (ICDM'03), IEEE Computer Society, USA, 2003, pp. 613-616.
  40. A. Amiri, D. toshare: Protecting sensitive knowledge withdata saniti-zation. Decision Support Systems, 43(1):181–191, 2007.
  41. V.S. Verykios, A. Elmagarmid, E. Bertino, Y. Saygin, , and E. Dasseni, “Association rule hiding”, IEEE Transactions on Knowledge and Data Engineering, 2004, 16(4): pp. 434-447.
  42. S.R.M. Oliveira, O.R. Zaıane, Y. Saygin, “Secure association rule sharing, advances in knowledge discovery and data mining, in: Proceedings of the 8th Pacific-Asia Conference (PAKDD2004), Sydney, Australia, 2004, pp.74–85.
  43. E. D. Pontikakis, A. A. Tsitsonis, and V. S. Verykios. An experimental study of distortion-based techniques for association rule hiding. In Proceedings of the 18th Conference on Database Security (DBSEC 2004), pages 325–339, 2004.
  44. Y. Chiang Li a, J. Shan Yeh b, C. Chang "MICF: An effective sanitization algorithm for hiding sensitive patterns on data mining" Advanced Engineering Informatics 21 (2007) 269–280.
  45. S. Liang Wang, B. Parikh, A. Jafari "Hiding informative association rule sets" Expert Systems with Applications 33 (2007) 316–323.
  46. C. Weng, S. Chen, H. Che Lo,” A Novel Algorithm for Completely Hiding Sensitive Association Rules” Eighth International Conference on Intelligent Systems Design and Applications 2008 IEEE.
  47. K. Duraiswamy, D. Manjula, N. Maheswari “A New Approach to Sensitive Rule Hiding” computer and information science Vol. 1, No. 3 August, 2008.
  48. Modi, C.N.; Rao, U.P.; Patel, D.R., “Maintaining privacy and data quality in privacy preserving association rule mining”, IEEE 2008 Seventh International Conference on Machine Learning and Applications, pp 1-6, 2010.
  49. R. Sugumar, C. Jayakumar, A. Rengarajan,” Design a Weight Based sorting distortion algorithm using Association rule Hiding for Privacy Preserving Data mining” R Sugumar et al, International Journal of Computer Science & Communication Networks,Vol 1(3), 270-276 (2011).
  50. Y. Kumar Jain, V. Kumar Yadav, G. S. Panday “An Efficient Association Rule Hiding Algorithm for Privacy Preserving Data Mining” International Journal on Computer Science and Engineering (IJCSE) Vol. 3 No. 7 July 2011.
  51. D. Jain, P. Khatri, R. Soni, and B. Kumar Chaurasia” Hiding Sensitive Association Rules without Altering the Support of Sensitive Item(s)” Second International Conference, CCSIT 2012 Bangalore, India, January 2- 4, 2012, pp. 500–509, 2012”
  52. S. keer, A. Singh “Hiding Sensitive Association Rule Using Clusters of Sensitive Association Rule” International Journal of Computer Science and Network (IJCSN) ISSN 2277-5420 Volume 1, Issue 3, June 2012.
  53. S. Sharma, P. Jain,” A Novel Data Mining Approach for Information Hiding” International Journal of Computers and Distributed Systems Vol. No.1, Issue 3, October 2012.
  54. M. Mahendran,R. Sugumar, K.Anbazhagan, R. Natarajan“An Efficient Algorithm for Privacy Preserving Data Mining Using Heuristic Approach” International Journal of Advanced Research in Computer and Communication Engineering Vol. 1, Issue 9, November 2012 .
  55. Sh. Komal, and T, Amit, and G. Amit, “Association Rule Hiding by Heuristic Approach to Reduce Side Effects & Hide Multiple R.H.S. Items”, International Journal of Computer Applications, Vol. 45, No. 1, 2012, pp. 0975-8887.
  56. Kasthuri S and Meyyappan T,” Hiding Sensitive Association Rule Using Heuristic Approach” International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.3, No.1, January 2013.
  57. Nikunj H. Domadiya, "Hiding sensitive association rules to maintain privacy and data quality in database"; Advance Computing Conference (IACC), 2013 IEEE 3rd International.
  58. Sh. S. Sambhaji, K. Pravin P , " Study of Mining and Hiding of Sensitive Association Rule " International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Volume 3, Issue 6, November-December 2014 ISSN 2278-6856F.
  59. Z. K. Abari1, M. N. Dehkordi, "Privacy Preserving in Association Rule Mining" ACSIJ Advances in Computer Science: an International Journal, Vol. 4, Issue 1, No.13 , January 2015.
  60. M. SakenianDehkordi, and M. NaderiDehkordi " Introducing an algorithm for use to hide sensitive association rules through perturb technique" Journal of AI and Data Mining, Published online. Accepted 18 January 2016.
  61. Z. Kiani Abari, M. Naderi Dehkordi, "Double Clustering Method in Hiding Association Rules" Journal of Advances in Computer Research Quarterly pISSN: Sari Branch, Islamic Azad University (Vol. 7, No. 12345-606x eISSN: 2345-6078, Sari, I.R.Iran February 2016), Pages: 67-8.
  62. Y.Saygin, V. S. Verykios, and C. Clifton, “Using Unknowns to Prevent Discovery of Association Rules,” ACM SIGMOD, vol.30(4), pp. 45–54, Dec. 2001.
  63. Y. Saygin, V. S. Verykios, and A. K. Elmagarmid, “Privacy preserving association rule mining,” In Proceedings of the 12th International Workshop on Research Issues in Data Engineering (2002), 151–158.
  64. S.L.Wang and A. Jafari, “Using unknowns for hiding sensitive predictive association rules”, In Proc. IEEE Int’l Conf. Information Reuse and Integration (IRI 2005), Aug. 2005, pp. 223-228.
  65. X. Sun and P. S. Yu. A border-based approach for hiding sensitive frequent item sets. In Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM 2005), pages 426–433, 2005.
  66. V. Moustakides and V. S. Verykios. A max min approach for hiding frequent item sets. In Workshops Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), pages 502–506, 2006.
  67. G. V. Moustakides, V. S. Verykios, "A Max Min approach for hiding frequent item sets" Data & Knowledge Engineering 65 (2008) 75–89.
  68. H. Quoc Le, S. Arch-int, and N. Arch-int “Association Rule Hiding Based on Intersection Lattice” Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2013, Article ID 210405, 11 pages.
  69. J. Vaidya, C. Clifton “Privacy Preserving Association Rule Mining in Vertically Partitioned Data” SIGKDD ’02 Edmonton, Alberta, Canada Copyright 2002 ACM 158113567.
  70. J. Vaidya, C. Clifton “Secure Set Intersection Cardinality with Application to Association Rule Mining” Department of Computer Sciences Purdue University 250 N University St West Lafayette, IN 47907-2066 March 15, 2004
  71. S. Zhong, “Privacy preserving algorithms for distributed mining of frequent item sets” Information Sciences 177 (2007) 490–503
  72. A. El-Sisi," Fast Cryptographic Privacy Preserving Association Rules Mining on Distributed Homogenous Database" The International Arab Journal of Information Technology, Vol. 7, No. 2, April 2010.
  73. M. G. Kaosar , R. Paulet, X. Yi” Fully homomorphic encryption based two-party association rule mining” Data & Knowledge Engineering 76 – 78 (2012) 1– 15.
  74. S. Z. Alborzi, A. Raj, and M. H. Saraee, “Privacy Preserving Mining of Association Rules on Horizontally distributed Databases” IPCSIT vol. 41 (2012) .
  75. A. Tomar, V. Richhariya, M. Ku. Mishra, “A Improved Privacy Preserving Algorithm Us-Ing Association Rule Mining In Centralized Da-Tabase” International Journal of Advanced Technology & Engineering Research (IJATER) VOLUME 2, ISSUE 2, MARCH 2012.
  76. M. Atallah, E. Bertino, A. Elmagarmid, M. Ibrahim, and V. S. Verykios, ”Disclosure limitation of sensitive rules”, In: Scheuermann P, ed. Proc. of the IEEE Knowledge and Data Exchange Workshop (KDEX'99). IEEE Computer society, 1999.pp. 45-52.
  77. A. Gkoulalas-Divanis and V.S. Verykios, “An Integer Programming Approach for Frequent Itemset Hiding,” In Proc. ACM Conf. Information and Knowledge Management (CIKM ’06), Nov. 2006.
  78. A. Gkoulalas-Divanis and V.S. Verykios, “Exact Knowledge Hiding through Database Extension,” IEEE Transactions on Knowledge and Data Engineering, vol. 21(5), pp. 699–713, May 2009.
  79. S.Vijayarani, A. Tamilarasi, R. SeethaLakshmi “Tabu Search based Association Rule Hiding” International Journal of Computer Applications (0975 – 8887) Volume 19– No.1, April 2011.
  80. S. Rizvi, J. Haritsa Maintaining Data Privacy in Association Rule Mining. VLDB Conference, 2002.
  81. A. Evfimievski, R. Srikant, R. Agrawal, J. Gehrke," Privacy preserving mining of association rules",Information Systems 29 (2004) 343–364.
  82. X. Chen, M. Orlowska, and X. Li “A new framework for privacy preserving data sharing” In: Proc. of the 4P th P IEEE ICDM Work shop: Privacy and Security Aspects of Data Mining. IEEE Computer Society, 2004. 47-56.
  83. Y. Guo “Reconstruction-Based Association Rule Hiding” Proceedings of SIGMOD2007 Ph.D Workshop on Innovative Database Research 2007(IDAR2007), June 10, 2007, Beijing, China.
  84. J. Lin , Y. Cheng, " Privacy preserving item set mining through noisy items " Expert Systems with Applications 36 (2009) 5711–5717.
  85. D. Zhu, X. Li, S. Wu,” Identity disclosure protection: A data reconstruction approach for privacy-preserving data mining” Decision Support Systems 48 (2009) 133 –140.
Index Terms

Computer Science
Information Sciences

Keywords

(DM) Data Mining (PPDM) Privacy Preserving Data Mining (ARM) Association Rules Mining (ARH) Association Rules Hiding (MST) minimum support threshold (MCT) minimum confidence threshold (SE) Side Effects and (SAR) Sensitive Association Rules .

Powered by PhDFocusTM