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Differential privacy data publishing in the big data platform of precise poverty alleviation

Abstract

In order to study the application of differential privacy data release for the data platform of precise poverty alleviation (PPA), in this study, the data was protected by using differential privacy protection algorithm, and combined with artificial neural network to construct the algorithm model. And then based on MATLAB simulation experiment, the operation effect of the simulation model was verified through multiple angles. From the relationship between budget and coefficient, it can be concluded that compared with extraction procedure and noprivacy by statistical test, the algorithm proposed in this study was found to be more practical, and the result was close to the original data, and the effect was better; the error rate was also the lowest, not higher than 0.075. Comparing the accuracy of the algorithm with other algorithms, the result showed that other methods made the precision lower, but the function mechanism designed in this study did not; from the perspective of time, it is found that the time consumption of algorithm designed in this study was greatly reduced compared with other methods. Through the research in this paper, the model designed by combining artificial neural network and differential privacy achieved the expected effect. Although there are some shortcomings in the experimental process, in general, it can provide direction and guidance for the subsequent PPA work, and its social development has important guiding significance.

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Correspondence to Suwei Gao.

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Communicated by Mu-Yen Chen.

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Gao, S., Zhou, C. Differential privacy data publishing in the big data platform of precise poverty alleviation. Soft Comput 24, 8139–8147 (2020). https://doi.org/10.1007/s00500-019-04352-1

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Keywords

  • Differential privacy
  • Precise poverty alleviation
  • Privacy budget
  • Error rate
  • Big data