Classical token-based authentication can play a significant role in the web security check without accessing the database. For example, JSON Web Tokens (JWT) has been used to support scenarios such as single-sign-on. However, with the development of quantum computer, the security of JWT relying on the RSA algorithm would be compromised. Therefore, we propose a protocol to realize network authentication utilizing quantum token. Inspired by the structure of classical JWT, the structure of quantum token also consists of three parts: header, payload and quantum information. After the user logs in successfully, the quantum token can be issued by the server. If the user presents the quantum token to access again during the validity period, the server can verify whether the quantum token is valid. Our quantum token protocol can detect eavesdropping and achieve identity authentication. We also conduct a security analysis of the proposed protocol by addressing possible motives of an Eavesdropper and conclude the approach to be resilient against a broad range of attacks.

Service bundling within enterprises and service cooperation outside enterprises are quite common on cloud. The correlations between services called Alliance Relation (AR) have great impact on the system QoS of service composition. Existing research have not fully and systematically considered the types of AR. Meanwhile, many works suffer from the low searching efficiency of finding the optimal matching AR for composite services. In this paper, we propose a novel approach called Q-ARIGraph-NSGA3, where we establish a multi-granularity optimization model on quotient space and an efficient matching search method in which a partition mechanism is applied to accelerate the generation of the graph. We expand the alliance relation types in service composition with service granulation and AR granulation employed concurrently. Extensive experiments are conducted on a real-world web service dataset, which demonstrate that our approach outperforms the state-of-the-art approaches in terms of effectiveness and efficiency.

With the rapid development of P2P lending in recent years, how to recommend bidding items to the bidder to meet its preferences will become a research hotspot, but there are few studies in this area. And since there is not a dominant scoring mechanism in P2P lending platform, it is an urgent to solve the problem of how to generate a reasonable score matrix. First of all, Crawling over more than 320 thousand tenders of the last two years from the PPDai.com to. Then, based on the deeply analysis of the data characteristics of the P2P lending areas, three different types of scoring matrix (Boolean, continuous and hybrid) are generated. Then, three kinds of classical collaborative filtering recommendation algorithms are used in the experiment. The results show that different types of scoring matrix has important influence on the performance of the recommendation algorithm, and the performance of cooperative filtering algorithms is different when applying in P2P lending compared with applying in some other fields. Finally, the experimental results are analyzed, and the relevant suggestions are given for the P2P lending.