Today, modern healthcare systems rely on advanced computational technologies, including cloud-based systems, to gather and examine personal health data on a large scale. The use of advanced cloud services technologies, such as software as a service, application as a service, is challenging for end-users of cloud systems to protect sensitive data in their health applications. According to the importance of publishing data in the cloud, the information should be recorded and handled somehow so that any individuals' identity remains hidden. Therefore, one of the critical privacy challenges is protecting the quality of published data and privacy-preserving on the healthcare cloud simultaneously. The K-anonymity technology is one of the prevalent methods used for privacy-preserving. In this article, we suggest a novel approach based on the clustering process using the K-means++ method to achieve an optimal k-anonymity algorithm. Also, we use the normal distribution function to delete data that is less frequent, which can be improved the quality of anonymized data. Extensive experiments show the proposed method has been able to reduce information loss 1.5 times and execution time 3.5 times compared to AKA and GCCG algorithms. Also, it is highly scalable than others.

中文翻译：

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一种在医疗云中保护隐私的基于聚类的匿名化方法

如今，现代医疗保健系统依靠先进的计算技术（包括基于云的系统）来大规模收集和检查个人健康数据。使用先进的云服务技术，例如软件即服务、应用即服务，对于云系统的最终用户保护其健康应用中的敏感数据具有挑战性。根据在云端发布数据的重要性，应该以某种方式记录和处理信息，以便隐藏任何个人的身份。因此，关键的隐私挑战之一是同时保护医疗云上已发布数据的质量和隐私保护。K-匿名技术是用于隐私保护的流行方法之一。在这篇文章中，我们提出了一种基于聚类过程的新方法，使用 K-means++ 方法来实现最佳 k-匿名算法。此外，我们使用正态分布函数删除不那么频繁的数据，这可以提高匿名数据的质量。大量实验表明，与 AKA 和 GCCG 算法相比，所提出的方法能够减少 1.5 倍的信息丢失和 3.5 倍的执行时间。此外，它比其他人具有高度的可扩展性。