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.
Summary
While location information sharing technology provides convenience for unmanned driving and journey navigation, user journey information sharing has also become a disaster for privacy information leakage. The traditional differential privacy method can only perturb the data entirely and cannot consider the design of data availability. In this paper, the difference privacy algorithm is improved by combining it with the Apriori algorithm, and the relevant perturbation is carried out after mining the associated data of the user's trip. In the face of possible data attacks, the privacy protection of the sensitive information of the user's actual data is ensured while the availability of the data is ensured. By testing 3000 trip data generated by experimental simulation, the results show that the correlation information between the original datasets is destroyed. However good availability is maintained after the Laplace data perturbation of the proposed algorithm for both simultaneous and multi‐person trips.
There are mainly three types of problems in Peer-to-Peer (P2P) networks such as free-riding nodes, group deception, and node bias. To solve this, the authors proposed an incentive mechanism for the P2P networks based on feature weighting and game theory. The mechanism first used the five comprehensive coefficients of node degree, node clustering coefficient, local clustering coefficient, all clustering coefficients, and correlation coefficients to form a feature clustering matrix through linear fusion; then, in order to maximize the overall revenue, a sparse matrix of revenue was generated through feature classification, noise reduction, mapping and iteration, highlighting the status of fully cooperative nodes; afterwards, combining group evolution and constraint rule sets, the authors determined group node dynamic adjustment rules, response service rules, and message query and forwarding rules, to maximize service efficiency; finally, a multilayered P2P dynamic service system was constructed to promote the active evolution of nodes, and avoid the negative migration. The simulation experiment was also performed to verity this mechanism. The research findings provide an effective idea for stimulating node behaviors, reducing negative node migration, and excellent node selection.
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