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Reseach Article

Classification of Multiparty Outsourced Data with Privacy Preservation

Published on May 2016 by Avinash Thube, Aniket Patil, Vinayak Shinde
National Conference on Advancements in Computer & Information Technology
Foundation of Computer Science USA
NCACIT2016 - Number 4
May 2016
Authors: Avinash Thube, Aniket Patil, Vinayak Shinde
746333ca-4738-4d37-9a91-e33dbe4040db

Avinash Thube, Aniket Patil, Vinayak Shinde . Classification of Multiparty Outsourced Data with Privacy Preservation. National Conference on Advancements in Computer & Information Technology. NCACIT2016, 4 (May 2016), 1-4.

@article{
author = { Avinash Thube, Aniket Patil, Vinayak Shinde },
title = { Classification of Multiparty Outsourced Data with Privacy Preservation },
journal = { National Conference on Advancements in Computer & Information Technology },
issue_date = { May 2016 },
volume = { NCACIT2016 },
number = { 4 },
month = { May },
year = { 2016 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /proceedings/ncacit2016/number4/24716-3055/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advancements in Computer & Information Technology
%A Avinash Thube
%A Aniket Patil
%A Vinayak Shinde
%T Classification of Multiparty Outsourced Data with Privacy Preservation
%J National Conference on Advancements in Computer & Information Technology
%@ 0975-8887
%V NCACIT2016
%N 4
%P 1-4
%D 2016
%I International Journal of Computer Applications
Abstract

Back-propagation Neural Network is one of the important machines learning technique for classification. Accuracy of any machine learning technique is improved with volume of datasets. Recent developments in computer networks provide such environment that multiple users connect with each other. This scenario leads in development of machine learning technique with collaborative or joint participation of these multiple users. In collaborative learning multiple parties participate jointly in learning where data is shared by users may contain sensitive information such as corporate data, health care data and personal data. There are chances of data leakage by any corrupt party or intruders. Users are concern about privacy of their datasets due to this main hurdle in collaborative machine learning technique many users are not interested to participate. In this project we are giving solution to privacy of individual users data by converting data in the form of cipher texts where users can use these cipher texts directly in the learning process. A machine learning system working on cipher texts needs various computations. Recent development in cloud computing provides good computing environment where we are utilizing these computations. In this way a system will be developed with neural network machine learning technique working on cipher text and utilizing cloud computing services.

References
  1. HIPPA, National Standards to Protect the Privacy of Personal Health Information, http://www. hhs. gov/ocr/hipaa/finalreg. html.
  2. S. Pearson, Y. Shen, and M. Mowbray, "A Privacy Manager for Cloud Computing," Proc. Int'l Conf. Cloud Computing (CloudCom), pp. 90-106, 2009.
  3. N. Schlitter, "A Protocol for Privacy Preserving Neural Network Learning on Horizontal Partitioned Data," Proc. Privacy Statistics in Databases (PSD '08), Sept. 2008.
  4. YogachandranRahulamathavan, Suresh Veluru, Kanapathippillai, Cumanan and MuttukrishnanRajarajan,"Privacy-Preserving Multi-Class Support Vector Machine for Outsourcing the Data Classification in Cloud", IEEE transaction on Dependable and Secure Computing, vol. 11, no. 5, September/October 2014
  5. AnakAgungPutriRatna, Ahmad Shaugi, Prima DewiPurnamasari, Muhammad Salman,"Analysis and comparison of MD5 and SHA-1 algorithm implementation in Simple-O authentication based security system", IEEE Conference on Quality in Research, 2013
  6. C. Lee Giles, Fellow, IEEE, Christian W. Omlin, and Karvel K. Thornber,"Equivalence in Knowledge Representation: Automata, Recurrent Neural Networks, and Dynamical Fuzzy Systems", IEEE, vol. 87, no. 9, september 1999
  7. Ming Li, Shucheng Yu, KuiRen, Wenjing Lou And Y. Thomas Hou,"Toward Privacy-Assured And SearchableCloud Data Storage Services", Proc. IEEE Network, July/August 2013
  8. Jiawei Yuan, ShuchngYu,"Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing", IEEE Transaction on Parallel and Distributed Systems,vol. 25,NO. 1, January 2014.
  9. Durand Cutler. "Transformations" (http://groups. csail. mit. edu/graphics/classes/6837/F03/lectures/04_transformations. ppt)(PowerPoint). Massachusetts Institute of Technology. Retrieved 12 September 2008
  10. A. Bansal, T. Chen, and S. Zhong, "Privacy Preserving Back-Propagation Neural Network Learning over Arbitrarily Parti-tioned Data," Neural Computing Applications, vol. 20, no. 1, pp. 143-150, Feb. 2011.
  11. C. R. P. Lippmann, "An introduction to computing with neural networks," IEEE Acoust. Speech Signal Process. Mag. , vol. 4, no. 2, pp. 4–22, Apr. 1987.
  12. D. J. Myers and R. A. Hutchinson, "Efficient implementation of piecewise linear activation function for digital VLSI neural networks," Electron. Lett. , vol. 25, pp. 1662–1663, 1989.
  13. Kasgar A. K. , AgrawalJitendra, SahuSantosh, 2012, "New Modified 256-bit MD5 Algorithm with SHA Compression Funct ion", IJCA (0975–8887) Volume 42 (12) , pp47-51.
  14. William Stallings, Cryptography and NetworkSecurity: Priciples and Practice,5th Edit ionPrent ice Hall; 5 edit ion (January 24, 2010)
Index Terms

Computer Science
Information Sciences

Keywords

Cloud Learning Neural Network Back-propagation Privacy Preserving Data Classification.