Privacy-Preserving Mining of Outsourced Transaction Databases for Association Rules Generation Using Paillier Encryption

Authors(2) :-Chaitali C. Khandate, Prof. Antara Bhattacharya

Cloud computing or Distributed Network uses the ideal model of information mining-as-an organization, using these it is in every way a prominent choice for associations sparing cash on the cost of adding to secure, supervise and keep up an IT establishment. An affiliation/store debilitated in mining limit can outsource its mining needs to expert center on a cloud server. In any case, both the alliance rules and thing set of the outsourced database are viewed as private property of the affiliation. The data proprietor encodes the data and sends to the server to shield the corporate security. Client sends mining inquiries to server, and after that server conducts data mining and sends mixed case to the client. To get honest to goodness illustration client unscrambles mixed case. In this paper, we consider the issue of outsourcing the connection oversees mining task inside a corporate security safeguarding structure. Therefore Privacy Preserving Data Mining is an examination domain stressed with the security chose from eventually identifiable information when considered for data mining. The Rob Frugal encryption strategy is familiar with beat the security vulnerabilities of outsourced information, which is focused on adjusted substitution figures for things likewise, including fake cases for database. In any case, it contains diverse fake illustrations which augment the breaking point overhead. To beat this issue, the proposed procedure fuses extension of weighted support in remarkable support of things to diminish the amount of fake illustrations and to overhaul the security level for outsourced information with less multifaceted nature. The fake trade table data is changed over into grid arrangement to decrease the limit overhead. Moreover the estimating attack and man in the middle ambush are possible on fundamental Rob modest count. To vanquish these strikes we utilize Pallier Encryption on after Rob Frugal encryption plot with a particular true objective to give protection saving outsourced mining. In our proposed work we improved the security as thing and thing set build ambush are unfeasible in light of the structure; additionally we diminish the dealing with time.

Authors and Affiliations

Chaitali C. Khandate
Department of Computer Science and Engineering, G. H. Raisoni institute of Technology and Engineering, Nagpur, Maharashtra, India
Prof. Antara Bhattacharya
Department of Computer Science and Engineering, G. H. Raisoni institute of Technology and Engineering, Nagpur, Maharashtra, India

Cloud Computing, Association rule mining, Privacy-preserving outsourcing, Rob Frugal

  1. W. K. Wong, D. W. Cheung, E. Hung, B. Kao, and N. Mamoulis, “Security in outsourcing of association rule mining,” in Proc. Int. Conf. Very Large Data Bases, 2007, pp. 111-122.
  2. G. I. Davida, D. L. Wells, and J. B. Kam. “ A database encryption system with sub keys.” ACM TODS, 6(2):312-328, 1981.
  3. J. He and M. Wang. Cryptography and relational database management systems. In IDEAS, 2001.
  4. B. Iyer, S. Mehrotra, E. Mykletun, G. Tsudik, and Y. Wu. A framework for efficient storage security in RDBMS. In EDBT, 2004.
  5. C. Tai, P. S. Yu, and M. Chen, “K-support anonymity based on pseudo taxonomy for outsourcing of frequent item set mining,” in Proc. Int. Knowledge Discovery Data Mining, 2010, pp. 473-482.
  6. F. Giannotti, L. V. Lakshmanan, A. Monreale, D. Pedreschi, and H.Wang, “Privacy preserving data mining from outsourced databases,” in Proc. SPCC2010 Conjunction with CPDP, 2010, pp. 411-426.
  7. M. Kantarcioglu and C. Clifton, “Privacy-preserving distributed mining of association rules on horizontally partitioned data,” IEEE Trans. Knowledge Data Eng., vol. 16, no. 9, pp. 1026-1037, Sep. 2004.
  8. S. J. Rizvi and J. R. Haritsa, “Maintaining data privacy in association rule mining”, in Proc. Int. Conf. Very Large Data Bases, 2002.
  9. A. Evfimievski, R. Srikant, R. Agrawal, J. Gehrke, “Privacy Preserving Mining of Association Rules”, Information System, 2004.
  10. H. Kargupta, S. Datta, Q. Wang, K. Sivakumar, “On the Privacy Preserving Properties of Random Data Perturbation Techniques”, In Proceedings of the 3rd International Conference on Data Mining, 2003.
  11. Z. Huang, W. Du, B. Chen, “Deriving Private Information from Randomized Data”, In Proceedings of the ACM SIGMOD Conference on Management of Data, 2005.
  12. S. L. Warner, “Randomized Response: A Survey Technique for Eliminating 999999999999Evasive Answer Bias”, J. Am. Stat. Assoc., 1965.
  13. A. Evfimievski, R. Srikant, R. Agrawal, J. Gehrk, “Privacy Preserving Mining of Association Rules”, In Proceedings the 8th ACM SIGKDD International Conference on Knowledge Discovery in Databases and Data Mining, 2002.
  14. Ram Ratan Ahirwal, Manoj Ahke Samrat Ashok, “Elliptic Curve Diffie- Hellman Key Exchange Algorithm for Securing Hypertext Information on Wide Area Network” et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 4 (2) , 2013, 363 368

Publication Details

Published in : Volume 2 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 509-516
Manuscript Number : CSEIT172353
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Chaitali C. Khandate, Prof. Antara Bhattacharya, "Privacy-Preserving Mining of Outsourced Transaction Databases for Association Rules Generation Using Paillier Encryption ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.509-516, May-June-2017.
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