Fuzzy-based random perturbation for real world medical datasets

Geetha, Mary A.

doi:10.1504/ijtmcp.2015.069749

Cited by 5 publications

(2 citation statements)

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“…TrMF has the four arguments, lower limit ‘a’, upper limit ‘d’, lower support limit ‘b’ and upper support limit ‘c’ where . Here, the argument and indicates the trapezium feet and the argument and indicates the flatted top end of trapezium [ 21 , 31 ]. TrMF is given in the Eq.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Association rule mining has not been applied and not tested for various other classification algorithms [ 20 ]. Randomization process for data perturbation has been generated from the fuzzy system based on level of privacy and various inputs [ 21 ]. Privacy preserving rewiring algorithm uses the fuzzy set technique for the process of data fuzzification.…”

Section: Related Workmentioning

confidence: 99%

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STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation

Kumar¹,

Premalatha²

2023

Distrib Parallel Databases

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Data sharing to the multiple organizations are essential for analysis in many situations. The shared data contains the individual’s private and sensitive information and results in privacy breach. To overcome the privacy challenges, privacy preserving data mining (PPDM) has progressed as a solution. This work addresses the problem of PPDM by proposing statistical transformation with intuitionistic fuzzy (STIF) algorithm for data perturbation. The STIF algorithm contains statistical methods weight of evidence, information value and intuitionistic fuzzy Gaussian membership function. The STIF algorithm is applied on three benchmark datasets adult income, bank marketing and lung cancer. The classifier models decision tree, random forest, extreme gradient boost and support vector machines are used for accuracy and performance analysis. The results show that the STIF algorithm achieves 99% of accuracy for adult income dataset and 100% accuracy for both bank marketing and lung cancer datasets. Further, the results highlights that the STIF algorithm outperforms in data perturbation capacity and privacy preserving capacity than the state-of-art algorithms without any information loss on both numerical and categorical data.

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