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This paper combines the differential privacy model to carry out hierarchical noise processing on the original data, and assigns different privacy budgets to each quasi-identifier attribute according to the privacy weight to achieve the purpose of hierarchical protection of differential privacy and protection of anonymous data sets. Laplace noise is used to process numerical data, and exponential mechanism is used to add noise to classified data. |
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