A Privacy Preserving Clustering On Alphanumeric Data

Authors

  • Patel Kevin  U.G.B.E. Student Computer Engineering, Sigma Institute of engineering, Bakrol, Gujarat, India
  • Patel Hiteshree  U.G.B.E. Student Computer Engineering, Sigma Institute of engineering, Bakrol, Gujarat, India
  • Patel Krishna C.  U.G.B.E. Student Computer Engineering, Sigma Institute of engineering, Bakrol, Gujarat, India
  • Patel Krishna J.  Assistant Professor, Computer Engineering, Sigma Institute of engineering, Bakrol, Gujarat, India
  • Mr. Kaushal Patel  Head of Department, Computer Engineering, Sigma Institute of engineering, Bakrol, Gujarat, India
  • Dr. Sheshang Degadwala  

DOI:

https://doi.org/10.32628/CI023

Keywords:

Privacy, Preserve, Data mining, Clustering, Perturbation

Abstract

Huge volume of detailed data is collected and analyzed by applications using data mining, sharing of these data is beneficial to the unauthorized persons. On the other hand, it is an important asset to business organization and government for decision making process. At the same time analyzing such data open threats to privacy if privacy is not preserved properly. Our project aims to reveal the information by protecting sensitive data. We are using three perturbation techniques to preserve the privacy of data. Perturbed dataset does not reveal the original data and also does not change the analysis results. Hence, privacy is preserved.

References

  1. Liming Li, Qishan Zhang, “A Privacy Preserving Clustering Technique Using Hybrid Data Transformation Method”, IEEE, 2009
  2. Ali Nan, YucelSayg?n, Erkay Sava, AycaAzg?nHinto Lu, Albert Levi, “Privacy Preserving Clustering on Horizontally Partitioned Data”, IEEE, 2006
  3. M Kalita, D K Bhattacharyya, M Dutta, “Privacy Preserving Clustering- A Hybrid Approach”, IEEE, 2008
  4. Fang-Yu Rao, Bharath K. Samanthula, Elisa Bertino, Xun Yi, Dongxi Liu, “Privacy-Preserving and Outsourced Multi-User k-Means Clustering”, IEEE, 2015
  5. Fan-rang Meng, Bin Liu, Chu-jiao Wang, “Privacy Preserving Clustering over Distributed Data”, IEEE, 2010
  6. Keng-PeiLin,“Privacy-preserving kernek-means clustering outsourcing with random transformation”, Springer, 2016
  7. Yu Xin, Zhi-QiangXie,  Jing Yang, “The privacy preserving method for dynamic trajectory releasing based on adaptive clustering”, Elsevier, 2016
  8. Alper Bilge, HuseyinPolat?, “A scalable privacy-preserving recommendation scheme via bisecting k-means clustering”, Elsevier, 2016
  9. Zhi-QiangGao, Long-Jun Zhang, “DPHKMS: An Efficient Hybrid Clustering Preserving Differential Privacy in Spark”, Springer, 2016

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-04-10

Issue

Volume 4, Issue 5, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Patel Kevin, Patel Hiteshree, Patel Krishna C., Patel Krishna J., Mr. Kaushal Patel, Dr. Sheshang Degadwala "A Privacy Preserving Clustering On Alphanumeric Data" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 5, pp.339-342, March-April-2018. Available at doi : https://doi.org/10.32628/CI023