DLFPM-SSO-PE: privacy-preserving and security of intermediate data in cloud storage

Sarala, V.; Shanmugapriya, P.

doi:10.1007/s10619-021-07352-z

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…The randomized function has been applied by the data miner to recognize the aggregated property in randomization method [ 11 ]. A frequent mining model based on the deep learning network approach and paillier encryption technique has been employed to reduce unauthorized data access, computation complexity and provides the data privacy [ 12 ]. This approach does not care for the data utility if the expectation results in more precise mining.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%