The study aims at discussing the security of medical data in the Internet era. By using k-anonymity (K-A) and differential privacy (DP), an algorithm model combining K-A and DP was proposed, which was simulated through the experiments. In the Magic and EIA datasets, the algorithm constructed was compared with K-A and the L-diversity model to verify the performance of the model. The model constructed based on DP had the lowest privacy-leakage risks, which increased with the number of identifiers in the Magic and EIA datasets, and the information disclosure was the least. In addition, in its usability analysis, it was found that its value was the most obviously improved and its operation efficiency was the highest. The K-A-DP algorithm can effectively reduce the risk of privacy leakage and information loss, and has achieved excellent results. Despite the deficiencies in the process of the experiment, the study still provides a reference for solving the problem of medical data security.

中文翻译：

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基于差分隐私技术的互联网医疗数据安全

本研究旨在探讨互联网时代医疗数据的安全性。利用k-匿名（KA）和差分隐私（DP），提出了一种KA和DP相结合的算法模型，并通过实验进行了仿真。在 Magic 和 EIA 数据集中，将构建的算法与 KA 和 L-diversity 模型进行比较，以验证模型的性能。基于 DP 构建的模型的隐私泄露风险最低，随着 Magic 和 EIA 数据集中标识符数量的增加而增加，信息泄露最少。此外，在其可用性分析中，发现其价值提升最为明显，运行效率最高。KA-DP算法可以有效降低隐私泄露和信息丢失的风险，取得了优异的效果。