Open Access   Article

Data Transformation Technique for Preserving Privacy in Data

Uma Shankar Rao Erothi1 , Sireesha Rodda2

1 Department of CSE, RAGHU Institute of Technology, Visakhapatnam, India.
2 Department of CSE, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-5 , Page no. 42-50, May-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i5.4250

Online published on May 31, 2018

Copyright © Uma Shankar Rao Erothi, Sireesha Rodda . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: Uma Shankar Rao Erothi, Sireesha Rodda, “Data Transformation Technique for Preserving Privacy in Data”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.42-50, 2018.

MLA Style Citation: Uma Shankar Rao Erothi, Sireesha Rodda "Data Transformation Technique for Preserving Privacy in Data." International Journal of Computer Sciences and Engineering 6.5 (2018): 42-50.

APA Style Citation: Uma Shankar Rao Erothi, Sireesha Rodda, (2018). Data Transformation Technique for Preserving Privacy in Data. International Journal of Computer Sciences and Engineering, 6(5), 42-50.

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Abstract

The increase of digitization has led to growing concerns over preserving privacy of sensitive data. The ubiquity of sensitive information in data sources such as financial transactions, commercial transactions, medical records, network communication etc., steered towards development of different privacy preserving techniques. In this paper, a novel data transformation technique has been proposed for providing efficient privacy preservation in the data. Inorder to provide privacy to data, the numeric attributes are transformed to the range [-1,1] while the characters or strings are transformed to binary strings. Data analysis over the transformed dataset provides the same result as that of the original dataset. The performance of the data transformation technique is evaluated on the datasets before and after transformation. Experiments on five standard datasets indicate high data utility of the proposed technique. The proposed technique is also evaluated on the standard network intrusion dataset NSL-KDD dataset to study the effectiveness of the proposed technique in intrusion detection domain and the results are analyzed. Privacy measures are evaluated to ascertain the degree of privacy offered by the proposed technique.

Key-Words / Index Term

Privacy Preservation, PPDM, Data Transformation, Network Intrusion Detection, Data Mining

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