Quantum authorization management (QAM) is the quantum scheme for privilege management infrastructure (PMI) problem. Privilege management (authorization management) includes authentication and authorization. Authentication is to verify a user's identity. Authorization is the process of verifying that a authenticated user has the authority to perform a operation, which is more fine-grained. In most classical schemes, the authority management center (AMC) manages the resources permissions for all network nodes within the jurisdiction. However, the existence of AMC may be the weakest link of the whole scheme. In this paper, a protocol for QAM without AMC is proposed based on entanglement swapping. In this protocol, Bob (the owner of resources) authenticates the legality of Alice (the user) and then shares the right key for the resources with Alice. Compared with the other existed QAM protocols, this protocol not only implements authentication, but also authorizes the user permissions to access certain resources or carry out certain actions. The authority division is extended to fin-grained rights division. The security is analyzed from the four aspects: the outsider's attack, the user's attack, authentication and comparison with the other two QAM protocols.
An excellent cardinality estimation can make the query optimiser produce a good execution plan. Although there are some studies on cardinality estimation, the prediction results of existing cardinality estimators are inaccurate and the query efficiency cannot be guaranteed as well. In particular, they are difficult to accurately obtain the complex relationships between multiple tables in complex database systems. When dealing with complex queries, the existing cardinality estimators cannot achieve good results. In this study, a novel cardinality estimator is proposed. It uses the core techniques with the BiLSTM network structure and adds the attention mechanism. First, the columns involved in the query statements in the training set are sampled and compressed into bitmaps. Then, the Word2vec model is used to embed the word vectors about the query statements. Finally, the BiLSTM network and attention mechanism are employed to deal with word vectors. The proposed model takes into consideration not only the correlation between tables but also the processing of complex predicates. Extensive experiments and the evaluation of BiLSTM-Attention Cardinality Estimator (BACE) on the IMDB datasets are conducted. The results show that the deep learning model can significantly improve the quality of cardinality estimation, which is a vital role in query optimisation for complex databases.
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