Privacy preservation based on rotation perturbation in weighted social networks

Li, Furong; Zhang, Ru; Xu, Yi; Li, Juan

doi:10.1109/iscit.2016.7751622

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…While the input perturbation uses one of the additive noise or multiplicative noise methods, the noise addition and rule hiding approaches [14][15][16] are applied together in output perturbation [17]. Input perturbation; can be applied one dimensional with additive perturbation [18,19], random response [20] and swapping [21][22][23][24], while condensation [25], random rotation [26][27][28][29][30], random projection [31] and geometric perturbation [32], can be applied multi-dimensionally.…”

Section: Literature Reviewsmentioning

confidence: 99%

A New Geometric Data Perturbation Method for Data Anonymization Based on Random Number Generators

Kanmaz

Aydın

Sertbaş

2021

JWE

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With the technology’s rapid development and its involvement in all areas of our lives, the volume and value of data have become a significant field of study. Valuation of the data to this extent has produced some consequences in terms of people’s knowledge. Data anonymization is the most important of these issues in terms of the security of personal data. Much work has been done in this area and continues to being done. In this study, we proposed a method called RSUGP for the anonymization of sensitive attributes. A new noise model based on random number generators has been proposed instead of the Gaussian noise or random noise methods, which are being used conventionally in geometric data perturbation. We tested our proposed RSUGP method with six different databases and four different classification methods for classification accuracy and attack resistance; then, we presented the results section. Experiments show that the proposed method was more successful than the other two classification accuracy, attack resistance, and runtime.

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