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In the field of cloud computing, most research on identity management has concentrated on protecting user data. However, users typically leave a trail when they access cloud services, and the resulting user traceability can potentially lead to the leakage of sensitive user information. Meanwhile, malicious users can do harm to cloud providers through the use of pseudonyms. To solve these problems, we introduce a reputation mechanism and design a reputation-based identity management model for cloud computing. In the model, pseudonyms are generated based on a reputation signature so as to guarantee the untraceability of pseudonyms, and a mechanism that calculates user reputation is proposed, which helps cloud service providers to identify malicious users. Analysis verifies that the model can ensure that users access cloud services anonymously and that cloud providers assess the credibility of users effectively without violating user privacy.

Nowadays more and more cloud infrastructure service providers are providing large numbers of service instances which are a combination of diversified resources, such as computing, storage, and network. However, for cloud infrastructure services, the lack of a description standard and the inadequate research of systematic discovery and selection methods have exposed difficulties in discovering and choosing services for users. First, considering the highly configurable properties of a cloud infrastructure service, the feature model method is used to describe such a service. Second, based on the description of the cloud infrastructure service, a systematic discovery and selection method for cloud infrastructure services are proposed. The automatic analysis techniques of the feature model are introduced to verify the model’s validity and to perform the matching of the service and demand models. Finally, we determine the critical decision metrics and their corresponding measurement methods for cloud infrastructure services, where the subjective and objective weighting results are combined to determine the weights of the decision metrics. The best matching instances from various providers are then ranked by their comprehensive evaluations. Experimental results show that the proposed methods can effectively improve the accuracy and efficiency of cloud infrastructure service discovery and selection.

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