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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.