International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Performance Analysis of Perturbation-based Privacy Preserving Techniques: An Experimental Perspective

  • Ritu Ratra (Department of Computer Science & Applications, Maharshi Dayanand University) ;
  • Preeti Gulia (Department of Computer Science & Applications, Maharshi Dayanand University) ;
  • Nasib Singh Gill (Department of Computer Science & Applications, Maharshi Dayanand University)
  • Received : 2023.10.05
  • Published : 2023.10.30
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Abstract

In the present scenario, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In Perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several perturbation strategies that may be used to protect data privacy. For this experiment, two perturbation techniques based on random projection and principal component analysis were used. These techniques include Improved Random Projection Perturbation (IRPP) and Enhanced Principal Component Analysis based Technique (EPCAT). The Naive Bayes classification algorithm is used for data mining approaches. These methods are employed to assess the precision, run time, and accuracy of the experimental results. The best perturbation method in the Nave-Bayes classification is determined to be a random projection-based technique (IRPP) for both the cardiovascular and hypothyroid datasets.

Keywords

Acknowledgement

The authors are thankful to the Department of Computer science & Applications, Maharshi Dayanand University, Rohtak, Haryana, India for its support. The authors are grateful to https://archive.ics.uci.edu/ml/index.php and https://www.kaggle.com/datasets for providing the datasets for experimental purposes.

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