SEMMI: Multi-party Security Decision-making Scheme Under the Internet of Medical Things

In the Internet of Medical Things, the intelligent auxiliary decision-making system uses machine learning algorithms to analyze medical data for disease diagnosis, auxiliary intervention, and analysis and early warning. However, in the process of medical data transmission, processing, and storage, a large amount of private information is also at risk of leakage. Therefore, this article proposes a smart classification and decision-making program in the Internet of Medical Things scenario-SEMMI, which can effectively deal with the risk of data leakage in the process of medical data processing. At the same time, it reduces the huge computing and storage pressure caused by encryption and decryption operations in medical institutions. In the scheme, data collection, processing, transmission, storage and calculation are completed by ciphertext. In addition, in view of the relatively weak computing and storage capabilities of sensor nodes, we use chaos theory to construct a stream cipher algorithm to ensure the security of transmission from sensor to user; the homomorphic encryption algorithm is used to ensure the computability of the ciphertext and the security of storage. Through security analysis, it can be concluded that this scheme can resist attacks from adversaries; at the same time, the experimental results show that the scheme has good performance in terms of calculation, storage overhead, accuracy, and so on.