A modified un-realisation approach for effective data perturbation. (10th February 2023)

• Record Type: Journal Article
• Authors: Murthy, T.S.; Gopalan, N.P.; Balaji, Banothu
• Is Part Of: International journal of intelligent enterprise. Volume 10:Number 2(2023)
• Page Start: 192
• Page End: 205
• Publication Date: 2023-02-10
• Subjects: un-realisation algorithm -- modified un-realisation algorithm -- MUA -- privacy preserving -- perturbation; Organizational learning -- Periodicals; Knowledge management -- Periodicals; Business intelligence -- Periodicals; Business enterprises -- Design -- Periodicals; Organizational effectiveness -- Periodicals; 658.005
• Languages: English
• ISSNs: 1745-3232

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.