Since existing dynamic multi-keyword ranked search schemes over encrypted data in cloud storage can not support semantic extension and do not have forward and backward security, a multi-keyword ranked search scheme over encrypted cloud data was proposed, which supported semantic search and achieved forward and backward security. The semantic extension of query keywords was achieved by constructing semantic relationship graph, the retrieval and dynamic update of data were achieved by use of tree-based index structure, the multi-keyword ranked search was achieved based on vector space model, and the extended index and query vectors were encrypted by using secure K-nearest neighbor algorithm. Security analysis indicates that the proposed scheme is secure under the known ciphertext model and achieves forward and backward security during dynamic update. Efficiency analysis and simulation experiments show that this scheme is superior to the same type schemes with the same security or function in server retrieval efficiency.

Aiming at the universality of individual interaction and the heterogeneity of individual social influence in opinion spreading, an opinion formation model of social network was proposed on the basis of Hegselmann-Krause model. By introducing the concepts of intimacy between individuals, interpersonal similarity and interaction strength, the individual interactive set was extended, the influence weight was reasonably quantified, and more realistic view of interaction rule was built. Through a series of simulation experiments, the effects of main parameters in the model on opinion evolution were analyzed. The simulation results indicate that group views can converge to the same and form consensus under different confidence thresholds. And the larger the confidence threshold is, the shorter the convergence time is. When confidence threshold is 0.2, convergence time is only 10. Meanwhile, extending the interactive set and increasing the strength of interpersonal similarity will promote consensus formation. Besides, when the clustering coefficient and the average degree of scale-free network are higher, the group views are more likely to produce convergence effect. The results are helpful to understand the dynamic process of opinion formation, and can guide social managers to make decisions and analysis.