The role of user defined function in privacy preserving data mining

  • Authors

    • G Manikandan SASTRA DEEMED University
    • A Vamsi Krishna SASTRA DEEMED University
    • P Lakshmana Sarvagna SASTRA DEEMED University
    2018-06-01
    https://doi.org/10.14419/ijet.v7i2.12202
  • Clustering, Misclassification error, Privacy, Scaling, Shearing, Translation
  • Data mining is used to retrieve the plugged in information from the huge data warehouse. Data mining techniques use wide variety of tools for extracting the required knowledge from the hefty data warehouse. Many organizations trust on the extracted knowledge for strategical decision making. In the downside these techniques also reveals some private information as part of the conversion process. Experts rely on various privacy preserving approaches to prevent the data disclosure. This paper primarily focuses on the use of a User-defined Function to maintain data privacy. The output of the proposed approach is compared with the 3-Dimensional geometric transformations such as Translation, Scaling and Shearing. From the experimental outcome it is evident that the proposed approach results in a minimal misclassification error when compared with the other data transformations.

     

  • References

    1. [1] Upadhyay, S. et al., “Privacy-preserving data mining with 3-D rotation transformationâ€, Journal of King Saud University – Computer and Information Sciences,pp.,(2016).

      [2] G. Manikandan, N. Sairam, C. Saranya and S. Jayashree, "A Hybrid Privacy Preserving Approach in Data Mining", Middle-East Journal of Scientific Research, pp.581-85, 2013

      [3] TamannaKachwala, Dr. L. K. Sharma, “A Literature analysis on Privacy Preserving Data Miningâ€, International Journal of Innovative Research in Computer and Communication Engineering, pp.2838-42, 2015.

      [4] Aristos Aristodimou, Athos Antoniades, Constantinos S. Pattichis, “Privacy-preserving data publishing of categorical data through k-anonymity and feature selectionâ€, Healthcare Technology Letters, pp.16-21, 2016.

      [5] C.Saranya, G.Manikandan, “A Study on Normalization Techniques for Privacy Preserving Data Miningâ€, International Journal of Engineering and Technology, pp. 2701-04, 2013.

      [6] Chen, K., Liu, L., “Geometric data perturbation for privacy-preserving outsourced data miningâ€. Knowledge Information Systems, pp.657-95, 2011.https://doi.org/10.1007/s10115-010-0362-4.

      [7] Vaidya, J., Shafiq, B., Fan, W., Mehmood, D., Lorenzi, D., “A random decision tree framework for privacy-preserving data miningâ€, IEEE Transaction Dependable Secure Computingâ€, pp.399-11, 2014.

      [8] G.Manikandan, N.Sairam, S.Jayashree, C.Saranya, “Achieving Data Privacy in a Distributed Environment Using Geometrical Transformationâ€, Middle East Journal of Scientific Research, pp.107-11, 2013.

      [9] G.Manikandan, N.Sairam, R.Sudan and B.Vaishnavi "Shearing based Data Transformation Approach for Privacy Preserving Clustering " Third International Conference on Computing Communication and Networking Technologies (ICCCNT-2012), SNS College of Engineering, Coimbatore,2012.

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  • How to Cite

    Manikandan, G., Vamsi Krishna, A., & Lakshmana Sarvagna, P. (2018). The role of user defined function in privacy preserving data mining. International Journal of Engineering & Technology, 7(2), 884-886. https://doi.org/10.14419/ijet.v7i2.12202