How to transform the growing medical big data into medical knowledge is a global topic. However, medical data contains a large amount of personal privacy information, especially electronic medical records, gene data and electroencephalography data; the current methods and tools for data sharing are not efficient or cannot be applied in real-life applications. Privacy disclosure has become the bottleneck of medical big data sharing. In this context, we conducted research of medical data from the data collection, data transport and data sharing to solve the key problems of privacy protection and put forward a privacy protection sharing platform called MNSSp3 (medical big data privacy protection platform based on Internet of things), which attempts to provide an effective medical data sharing solution with the privacy protection algorithms for different data types and support for data analytics. The platform focuses on the transmission and sharing security of medical big data to provide users with mining methods and realizes the separation of data and users to ensure the security of medical data. Moreover, the platform also provides users with the capacity to upload privacy algorithms independently. We discussed the requirements and the design components of the platform, then three case studies were presented to verify the functionality of the platform, and the results of the experiments show clearly the benefit and practicality of the proposed platform.

如何将不断增长的医学大数据转化为医学知识是一个全球性的话题。但是，医疗数据包含大量的个人隐私信息，尤其是电子医疗记录，基因数据和脑电图数据。当前的数据共享方法和工具效率不高，或者无法在现实生活中应用。隐私披露已成为医疗大数据共享的瓶颈。在此背景下，我们从数据收集，数据传输和数据共享方面对医学数据进行了研究，以解决隐私保护的关键问题，并提出了一个名为MNSSp3的隐私保护共享平台（基于物联网的医学大数据隐私保护平台）。 ），它试图通过针对不同数据类型的隐私保护算法以及对数据分析的支持，提供有效的医学数据共享解决方案。该平台专注于医疗大数据的传输和共享安全，为用户提供挖掘方法，实现数据与用户的分离，确保医疗数据的安全。此外，该平台还为用户提供了独立上传隐私算法的能力。我们讨论了平台的要求和设计组件，然后进行了三个案例研究以验证平台的功能，并且实验结果清楚地表明了该平台的好处和实用性。该平台专注于医疗大数据的传输和共享安全，为用户提供挖掘方法，实现数据与用户的分离，确保医疗数据的安全。此外，该平台还为用户提供了独立上传隐私算法的能力。我们讨论了平台的要求和设计组件，然后进行了三个案例研究以验证平台的功能，并且实验结果清楚地表明了该平台的好处和实用性。该平台专注于医疗大数据的传输和共享安全，为用户提供挖掘方法，实现数据与用户的分离，确保医疗数据的安全。此外，该平台还为用户提供了独立上传隐私算法的能力。我们讨论了平台的要求和设计组件，然后进行了三个案例研究以验证平台的功能，并且实验结果清楚地表明了该平台的好处和实用性。