多项微数据隐私分析中的重组设计

Shu-Mei Wan, Danny Wen-Yaw Chung, Monica Mayeni Manurung, Kwang-Hwa Chang, Chien-Hua Wu
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引用次数: 0

摘要

在本文中,我们使用重组设计来处理保护隐私和从传播数据中进行统计推断的双重目标。通过干扰数据来保护患者的隐私并不困难。问题是要以一种既保护隐私又对研究有用的方式来干扰数据。通过应用重组设计,数据集通过预先指定的转移概率矩阵与实际组关联的虚拟组一起发布。建议重组设计的停滞概率较小,以达到较小的披露风险和更高的假设检验能力。发布的数据中检验统计量的幂随着停滞概率离开0.5而增加。如果更多的准标识符被重新定位,披露风险可以进一步降低。以全国健康保险研究数据库为例,说明了利用重组设计来保护隐私并进行统计推断。
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Regrouped design in privacy analysis for multinomial microdata
In this paper, we are dealing with the dual goals for protecting privacy and making statistical inferences from the disseminated data using the regrouped design. It is not difficult to protect the privacy of patients by perturbing data. The problem is to perturb the data in such a way that privacy is protected, and also, the released data are useful for research. By applying the regrouped design, the dataset is released with the dummy groups associated with the actual groups via a pre‐specified transition probability matrix. Small stagnation probabilities of regrouped design are recommended to reach a small disclosure risk and a higher power of hypothesis testing. The power of test statistic in the released data increases as the stagnation probabilities depart from 0.5. The disclosure risk can be reduced further if more quasi‐identifiers are relocated. An example of National Health Insurance Research Database is given to illustrate the use of the regrouped design to protect the privacy and make the statistical inference.
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