The collection and analysis of patient cases can effectively help researchers to extract case feature and to achieve the objectives of precision medicine, but it may cause privacy issues for patients. Although encryption is a good way to protect privacy, it is not conducive to the sharing and analysis of medical cases. In order to address this problem, this paper proposes a federated learning verification model, which combines blockchain technology, homomorphic encryption, and federated learning technology to effectively solve privacy issues. Moreover, we present a FL-EM-GMM Algorithm (Federated Learning Expectation Maximization Gaussian Mixture Model Algorithm), which can make model training without data exchange for protecting patient’s privacy. Finally, we conducted experiments on the federated task of datasets from two organizations in our model system, where the data has the same sample ID with different subset features, and this system is capable of handling privacy and security issues. The results show that the model was trained by our system with better usability, security, and higher efficiency, which is compared with the model trained by traditional machine learning methods.
The connecting of things to the Internet makes it possible for smart things to access all kinds of Web services. However, smart things are energy-limited, and suitable selection of Web services will consume less resources. In this paper, we study the problem of selecting some Web service from the candidate set. We formulate this selection of Web services for smart things as single-source many-target shortest path problem. We design algorithms based on the Dijkstra and breadth-first search algorithms, propose an efficient pruning algorithm for breadth-first search, and analyze their performance of number of iterations andI/Ocost. Our empirical evaluation on real-life graphs shows that our pruning algorithm is more efficient than the breadth-first search algorithm.
Multi-node cooperative sensing can effectively improve the performance of spectrum sensing. Multi-node cooperation will generate a large number of local data, and each node will send its own sensing data to the fusion center. The fusion center will fuse the local sensing results and make a global decision. Therefore, the more nodes, the more data, when the number of nodes is large, the global decision will be delayed. In order to achieve the real-time spectrum sensing, the fusion center needs to quickly fuse the data of each node. In this article, a fast algorithm of big data fusion is proposed to improve the real-time performance of the global decision. The algorithm improves the computing speed by reducing repeated computation. The reinforcement learning mechanism is used to mark the processed data. When the same environment parameter appears, the fusion center can directly call the nodes under the parameter environment, without having to conduct the sensing operation again. This greatly reduces the amount of data processed and improves the data processing efficiency of the fusion center. Experimental results show that the algorithm in this article can reduce the computation time while improving the sensing performance.
With the rapid development of mobile medical, how to establish an effective security mechanism to protect data security and privacy while users enjoy medical services has become an urgent problem to be solved. Aiming at the easy leakage of privacy in mobile medical terminals and untrustworthy data, we make use of a role-separated mechanism to generate trusted anonymous certificates. We propose a lightweight identity authentication scheme and adopt blockchain to protect the security of medical data. Meanwhile, in view of the problems of transparency and visibility of blockchain information, we adapt the searchable encryption algorithm to realize ciphertext processing in the whole life cycle. Experiments show that our scheme can reduce the cost of computation on the basis of ensuring traffic. In the process of dynamic updating of ciphertext keywords, except the keyword identifier, less information is leaked to the server, which protects privacy of users.
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