Fuzzy set based data publishing for privacy preservation

Xie, Mengbo; Qi, Qian

doi:10.1109/snpd.2016.7515960

Cited by 1 publication

(2 citation statements)

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“…In this perspective, fuzzy transformation method is used to distort the sensitive/quasi attributes values by using the fuzzy membership function. The process of generating membership values for a fuzzy variable using membership function is termed as fuzzification [27,28]. A membership function defines the mapping of membership value of input space between 0 and 1.…”

Section: Fuzzification and Anonymization Operation On Sensitivementioning

confidence: 99%

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Clustering based Privacy Preserving of Big Data using Fuzzification and Anonymization Operation

Khan¹,

Iqbal²,

Faizullah³

et al. 2019

IJACSA

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Big Data is used by data miner for analysis purpose which may contain sensitive information. During the procedures it raises certain privacy challenges for researchers. The existing privacy preserving methods use different algorithms that results into limitation of data reconstruction while securing the sensitive data. This paper presents a clustering based privacy preservation probabilistic model of big data to secure sensitive information..model to attain minimum perturbation and maximum privacy. In our model, sensitive information is secured after identifying the sensitive data from data clusters to modify or generalize it.The resulting dataset is analysed to calculate the accuracy level of our model in terms of hidden data, lossed data as result of reconstruction. Extensive experiements are carried out in order to demonstrate the results of our proposed model. Clustering based Privacy preservation of individual data in big data with minimum perturbation and successful reconstruction highlights the significance of our model in addition to the use of standard performance evaluation measures.

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Section: Fuzzification and Anonymization Operation On Sensitivementioning

confidence: 99%

“…Fuzzy based experimental results were better than kanonymity method in context of loss and performance. The performance and information loss are minimized 48% to 59% and 40% to 50% respectively [18].…”

Section: Literature Reviewmentioning

confidence: 99%