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Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

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Privacy Preserving Informative Association Rule Mining

by Kshitij Pathak, Sanjay Silakari, Narendra S. Chaudhari
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 8
Year of Publication: 2017
Authors: Kshitij Pathak, Sanjay Silakari, Narendra S. Chaudhari
10.5120/ijais2017451717

Kshitij Pathak, Sanjay Silakari, Narendra S. Chaudhari . Privacy Preserving Informative Association Rule Mining. International Journal of Applied Information Systems. 12, 8 ( Nov 2017), 1-7. DOI=10.5120/ijais2017451717

@article{ 10.5120/ijais2017451717,
author = { Kshitij Pathak, Sanjay Silakari, Narendra S. Chaudhari },
title = { Privacy Preserving Informative Association Rule Mining },
journal = { International Journal of Applied Information Systems },
issue_date = { Nov 2017 },
volume = { 12 },
number = { 8 },
month = { Nov },
year = { 2017 },
issn = { 2249-0868 },
pages = { 1-7 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number8/1006-2017451717/ },
doi = { 10.5120/ijais2017451717 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:08:23.101626+05:30
%A Kshitij Pathak
%A Sanjay Silakari
%A Narendra S. Chaudhari
%T Privacy Preserving Informative Association Rule Mining
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 8
%P 1-7
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Privacy preserving data mining has two major directions: one is the protection of private data, i.e., data hiding in the database whereas another one is the protection of sensitive rule (Knowledge) contained in data known as knowledge hiding in the database. This research work focuses on protection of sensitive association rule. Corporation individual & other may get mutual benefit by sharing their data, but at the same time, they would like to be sure that their sensitive data remains private or not disclosed, i.e., hiding sensitive association rules. Approaches need to be given sensitive association rule in advance to hide them, i.e., mining is repaired. However, for some application pre-process of these sensitive association rules is combined with hiding process when predictive items are given, i.e., hiding informative association rule set. In this work, we propose two algorithms ISLFASTPREDICTIVE, DSRFASTPREDICTIVE to hide informative association rule with n-items. Earlier work hided 2-item association rules. Algorithms proposed in the paper execute faster than ISL & DSR algorithms prepared earlier as well as a side effect have been reduced. ISLFASTPREDICTIVE and DSRFASTPREDICTIVE algorithms work better as database scans are reduced since transaction list of elements is used in algorithms, i.e., a list of the transaction which supports itemsets and selection of transactions are done on the basis of presence of frequent itemsets.

References
  1. Nabil R Adam and John C Worthmann. Security-control methods for statistical databases: a comparative study. ACM Computing Surveys (CSUR), 21(4):515–556, 1989.
  2. Charu C Aggarwal. On k-anonymity and the curse of dimensionality. In Proceedings of the 31st international conference on Very large data bases, pages 901–909. VLDB Endowment, 2005.
  3. Charu C Aggarwal and Philip S Yu. On privacypreservation of text and sparse binary data with sketches. In Proceedings of the 2007 SIAM International Conference on Data Mining, pages 57–67. SIAM, 2007.
  4. Dakshi Agrawal and Charu C Aggarwal. On the design and quantification of privacy preserving data mining algorithms. In Proceedings of the twentieth ACM SIGMODSIGACT- SIGART symposium on Principles of database systems, pages 247–255. ACM, 2001.
  5. Rakesh Agrawal, Roberto Bayardo, Christos Faloutsos, Gerald George Kiernan, Ralf Rantzau, and Ramakrishnan Srikant. Auditing compliance with a hippocratic database, October 5 2010. US Patent 7,810,142.
  6. Rakesh Agrawal, Ramakrishnan Srikant, et al. Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB, volume 1215, pages 487– 499, 1994.
  7. Rakesh Agrawal, Ramakrishnan Srikant, and Dilys Thomas. Privacy preserving olap. In Proceedings of the 2005 ACM SIGMOD international conference on Management of data, pages 251–262. ACM, 2005.
  8. Mike Atallah, Elisa Bertino, Ahmed Elmagarmid, Mohamed Ibrahim, and Vassilios Verykios. Disclosure limitation of sensitive rules. In Knowledge and Data Engineering Exchange, 1999.(KDEX’99) Proceedings. 1999 Workshop on, pages 45–52. IEEE, 1999.
  9. Xia Chen, Maria Orlowska, and Xue Li. A new framework of privacy preserving data sharing. In Proc. of the 4th IEEE ICDM Workshop: Privacy and Security Aspects of Data Mining. IEEE Computer Society, pages 47–56, 2004.
  10. Chris Clifton and Don Marks. Security and privacy implications of data mining. In ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, pages 15–19, 1996.
  11. Qin Ding, Qiang Ding, and William Perrizo. Parman efficient algorithm to mine association rules from spatial data. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(6):1513–1524, 2008.
  12. Aris Gkoulalas-Divanis and Vassilios S Verykios. Exact knowledge hiding through database extension. IEEE Transactions on Knowledge and Data Engineering, 21(5):699– 713, 2009.
  13. Michael Grean and Michael Shaw. Supply-chain partnership between p&g and wal-mart. E-Business Management, pages 155–171, 2002.
  14. Yuhong Guo. Reconstruction-based association rule hiding. In Proceedings of SIGMOD2007 Ph. D. Workshop on Innovative Database Research, volume 2007, pages 51–56, 2007.
  15. Yuhong Guo, Yuhai Tong, Shiwei Tang, and Dongqing Yang. A fp-tree-based method for inverse frequent set mining. In British National Conference on Databases, pages 152–163. Springer, 2006.
  16. Michael Hahsler, Christian Buchta, Bettina Gruen, and Kurt Hornik. arules: Mining Association Rules and Frequent Itemsets, 2017. R package version 1.5-2.
  17. Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, and Christian Buchta. The arules r-package ecosystem: Analyzing interesting patterns from large transaction datasets. Journal of Machine Learning Research, 12:1977–1981, 2011.
  18. Michael Hahsler, Bettina Gruen, and Kurt Hornik. arules – A computational environment for mining association rules and frequent item sets. Journal of Statistical Software, 14(15):1–25, October 2005.
  19. Christopher T Heun. When to share: Wal-mart and other companies reassess their data-sharing strategies. Information Week, 2001.
  20. Dhyanendra Jain, Pallavi Khatri, Rishi Soni, and Brijesh Kumar Chaurasia. Hiding sensitive association rules without altering the support of sensitive item (s). Advances in Computer Science and Information Technology. Networks and Communications, pages 500–509, 2012.
  21. Dejiang Jin and Sotirios G Ziavras. A super-programming approach for mining association rules in parallel on pc clusters. IEEE Transactions on Parallel and Distributed Systems, 15(9):783–794, 2004.
  22. Murat Kantarcioglu and Chris Clifton. Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE transactions on knowledge and data engineering, 16(9):1026–1037, 2004.
  23. Ashwin Machanavajjhala, Johannes Gehrke, Daniel Kifer, and Muthuramakrishnan Venkitasubramaniam. l-diversity: Privacy beyond k-anonymity. In Data Engineering, 2006. ICDE’06. Proceedings of the 22nd International Conference on, pages 24–24. IEEE, 2006.
  24. Stanley RM Oliveira and Osmar R Zaiane. Privacy preserving frequent itemset mining. In Proceedings of the IEEE international conference on Privacy, security and data mining-Volume 14, pages 43–54. Australian Computer Society, Inc., 2002.
  25. Stanley RM Oliveira and Osmar R Za¨iane. Protecting sensitive knowledge by data sanitization. In Data Mining, 2003. ICDM 2003. Third IEEE International Conference on, pages 613–616. IEEE, 2003.
  26. Kshitij Pathak, Narendra S Chaudhari, and Aruna Tiwari. Privacy preserving association rule mining by introducing concept of impact factor. In Industrial Electronics and Applications (ICIEA), 2012 7th IEEE Conference on, pages 1458–1461. IEEE, 2012.
  27. Kshitij Pathak, Aruna Tiwari, and Narendra S Chaudhari. Computational complexity of association rule hiding algorithms. Evolution in Networks and Computer Communications, (1).
  28. Kshitij Pathak, Aruna Tiwari, and Narendra S Chaudhari. A reduction of 3-sat problem from optimal sanitization in association rule hiding. In Emerging Trends in Networks and Computer Communications (ETNCC), 2011 International Conference on, pages 43–46. IEEE, 2011.
  29. Emmanuel Pontikakis, Achilleas Tsitsonis, and Vassilios Verykios. An experimental study of distortion-based techniques for association rule hiding. Research Directions in Data and Applications Security XVIII, pages 325–339, 2004.
  30. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2017.
  31. Y¨ucel Saygin, Vassilios S Verykios, and Chris Clifton. Using unknowns to prevent discovery of association rules. Acm Sigmod Record, 30(4):45–54, 2001.
  32. Xingzhi Sun and Philip S Yu. A border-based approach for hiding sensitive frequent itemsets. In Data Mining, Fifth IEEE International Conference on, pages 8–pp. IEEE, 2005.
  33. Vassilios S Verykios. Association rule hiding methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1):28–36, 2013.
  34. Vassilios S Verykios, Elisa Bertino, Igor Nai Fovino, Loredana Parasiliti Provenza, Yucel Saygin, and Yannis Theodoridis. State-of-the-art in privacy preserving data mining. ACM Sigmod Record, 33(1):50–57, 2004.
  35. Vassilios S Verykios, Ahmed K Elmagarmid, Elisa Bertino, Y¨ucel Saygin, and Elena Dasseni. Association rule hiding. IEEE Transactions on knowledge and data engineering, 16(4):434–447, 2004.
  36. En Tzu Wang, Guanling Lee, and Yu Tzu Lin. A novel method for protecting sensitive knowledge in association rules mining. In Computer Software and Applications Conference, 2005. COMPSAC 2005. 29th Annual International, volume 1, pages 511–516. IEEE, 2005.
  37. Shyue-Liang Wang. Maintenance of sanitizing informative association rules. Expert Systems with Applications, 36(2):4006–4012, 2009.
  38. Shyue-Liang Wang, Tzung-Pei Hong, Yu-Chuan Tsai, and Hung-Yu Kao. Multi-table association rules hiding. In Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on, pages 1298–1302. IEEE, 2010.
  39. Shyue-Liang Wang, Bhavesh Parikh, and Ayat Jafari. Hiding informative association rule sets. Expert Systems with Applications, 33(2):316–323, 2007.
  40. Yongge Wang and Xintao Wu. Approximate inverse frequent itemset mining: Privacy, complexity, and approximation. In Data Mining, Fifth IEEE International Conference on, pages 8–pp. IEEE, 2005.
  41. Junjie Wu, Shiwei Zhu, Hui Xiong, Jian Chen, and Jianming Zhu. Adapting the right measures for pattern discovery: A unified view. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(4):1203–1214, 2012.
  42. Yi-HungWu, Chia-Ming Chiang, and Arbee LP Chen. Hiding sensitive association rules with limited side effects. IEEE Transactions on Knowledge and Data engineering, 19(1), 2007.
  43. Xiaoming Zhang. Knowledge hiding in data mining by transaction adding and removing. In Computer Software and Applications Conference, 2007. COMPSAC 2007. 31st Annual International, volume 1, pages 233–240. IEEE, 2007.
  44. Cheng Zheng. An incremental updating technique for mining indirect association rules. In Machine Learning and Cybernetics, 2008 International Conference on, volume 1, pages 217–221. IEEE, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Informative Association Rules Knowledge Hiding in Database Frequent Itemset Privacy-Preserving Data Mining Sensitive Association Rules

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