A modified un-realisation approach for effective data perturbation

T.S. Murthy, N.P. Gopalan and Banothu Balaji

International Journal of Intelligent Enterprise, 2023, vol. 10, issue 2, 192-205

Abstract: In recent times, data has been evolving from multiple sources like social media, Facebook, Twitter, etc. in large volumes and acquiring in multiple forms. These data have multi-dimensional sensitive features from different resources entail that privacy preserving is a significant research issue. In this context, un-realisation algorithms have evolved to hide the collected data with the addition of noise to them to generate a distorted dataset while attempting privacy preservation. In this paper, a novel modified un-realisation algorithm has been proposed to generate a distorted dataset by removing duplicate elements in the dataset decreasing computational time of decision tree construction process. These techniques add noise to the original data and generate a distorted dataset by using a un-realisation algorithm. This novel approach converts the original sample datasets into different perturbed datasets by inducing the noise through set theory. It experimentally produces better results than un-realisation algorithm in terms of CPU execution time and space complexity.

Keywords: un-realisation algorithm; modified un-realisation algorithm; MUA; privacy preserving; perturbation. (search for similar items in EconPapers)
Date: 2023
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