Authors(2) :-Suganya. R, M. Nizar Ahmed

Data Mining has wide applications in many areas such as medicine, scientific, banking, research and among government agencies. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. Classification is one of the commonly used tasks in data mining applications However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy-preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed protocol protects the confidentiality of data, privacy of user’s input query, and hides the data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our proposed protocol using a real-world dataset under different parameter settings. The proposed system mainly focuses on information security in insurance company. They can encrypt the customer information and stored it in database. When data are encrypted, any data mining tasks becomes very challenging before decrypting data. Classification can apply to the customer records. This protects the customers’ sensitive information.

Authors and Affiliations

Suganya. R
Computer Science Department, PPG Institute of Technology, Coimbatore, Tamil Nadu, India
M. Nizar Ahmed
Computer Science Department, PPG Institute of Technology, Coimbatore, Tamil Nadu, India

Data Mining, Encrypted Database, Security, K-NNclassifier

  1. P. Mell and T. Grance, “The NIST definition of cloud computing (draft),” NIST Special Publication, vol. 800, p. 145, 2011.
  2. S. De Capitani di Vimercati, S. Foresti, and P. Samarati, “Managing and accessing data in the cloud: Privacy risks and approaches,” in Proc. 7th Int. Conf. Risk Security Internet Syst., 2012, pp. 1–9.
  3. P. Williams, R. Sion, and B. Carbunar, “Building castles out of mud: Practical access pattern privacy and correctness on untrusted storage,” in Proc. 15th ACM Conf. Comput. Commun. Security, 2008, pp. 139–148.
  4. P. Paillier, “Public key cryptosystems based on composite degree residuosity classes,” in Proc. 17th Int. Conf. Theory Appl. Cryptographic Techn., 1999, pp. 223–238.
  5. B. K. Samanthula, Y. Elmehdwi, and W. Jiang, “k-nearest neighbor classification over semantically secure encrypted relational data,” eprint arXiv:1403.5001, 2014.
  6. C. Gentry, “Fully homomorphic encryption using ideal lattices,” in Proc. 41st Annu. ACM Sympos. Theory Comput., 2009, pp. 169–178.
  7. C. Gentry and S. Halevi, “Implementing gentry’s fully-homomorphic encryption scheme,” in Proc. 30th Annu. Int. Conf. Theory Appl. Cryptographic Techn.: Adv. Cryptol., 2011, pp. 129 148.
  8. A. Shamir, “How to share a secret,” Commun. ACM, vol. 22, pp. 612–613, 1979.
  9. D. Bogdanov, S. Laur, and J. Willemson, “Sharemind: A framework for fast privacy preserving computations,” in Proc. 13th Eur. Symp. Res. Comput. Security: Comput. Security, 2008, pp. 192–206.
  10. R. Agrawal and R. Srikant, “Privacy-preserving data mining,” ACM Sigmod Rec., vol. 29, pp. 439–450, 2000.
  11. Y. Lindell and B. Pinkas, “Privacy preserving data mining,” in Proc. 20th Annu. Int. Cryptol. Conf. Adv. Cryptol., 2000, pp. 36–54.
  12. P. Zhang, Y. Tong, S. Tang, and D. Yang, “Privacy preserving Naive Bayes classification,” in Proc. 1st Int. Conf. Adv. Data Mining Appl., 2005, pp. 744–752.
  13. A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke, “Privacy preserving mining of association rules,” Inf. Syst., vol. 29, no. 4, pp. 343–364, 2004.
  14. R. J. Bayardo and R. Agrawal, “Data privacy through optimal kanonymization,” in Proc. IEEE 21st Int. Conf. Data Eng., 2005, pp. 217–228.

Publication Details

Published in : Volume 2 | Issue 3 | May-June 2016
Date of Publication : 2016-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 284-291
Manuscript Number : IJSRSET162377
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Suganya. R, M. Nizar Ahmed, " DATA MINING OVER ENCRYPTED DATA ON CLOUD, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 3, pp.284-291, May-June-2016.
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