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Cloud computing is a new paradigm which enables users to reduce their costs and is advantageous to both the serving and served organizations. However, security issue is a major concern in the adoption of cloud computing. The most effective way of protecting cloud computing services, resources and users is access control. This paper intends to provide a trust-based access control mechanism for cloud computing considering its multi-domain aspects. Firstly, trust is introduced into cloud computing environment and trust relationships between users and cloud platform are built. It also analyzes the difference between intra-domain trust and inter-domain trust. Furthermore, a role-based access control framework combined with trust degree in multi-domain is given from this paper. Access control in local domain directly applies RBAC model combined with trust degree, whereas in multi-domain it contains the conception of role translation. The simulation results show that the proposed method is more suitable to cloud environment and definitely can improve the reliability and validity of the system.

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Experimental data are often very complex since the underlying dynamical system may be unknown and the data may heavily be corrupted by noise. It is a crucial task to properly analyze data to get maximal information of the underlying dynamical system. This paper presents a novel principal component analysis (PCA) method based on symplectic geometry, called symplectic PCA (SPCA), to study nonlinear time series. Being nonlinear, it is different from the traditional PCA method based on linear singular value decomposition (SVD). It is thus perceived to be able to better represent nonlinear, especially chaotic data, than PCA. Using the chaotic Lorenz time series data, we show that this is indeed the case. Furthermore, we show that SPCA can conveniently reduce measurement noise.

To protect the copyright of digital image, this paper proposed a combined Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) based watermarking scheme. To embed the watermark, the cover image was decomposed by a 2-level DWT, and the HL2 sub-band coefficient was divided into 4x4 blocks, then the DCT was performed on each of these blocks. The watermark bit was embedded by predefined pattern_0 or pattern_1 on the middle band coefficients of DCT. After watermark insertion, inverse DCT was applied to each of the 4x4 blocks of HL2 sub-band coefficient, and inverse DWT was applied to obtain the watermarked image. For watermark extraction, the watermarked image, which may be attacked by various image attacks, was decomposed with 2-level DWT and DCT similarly as watermark embedding process, then correlation between middle band coefficients of block DCT and the predefined pattern (pattern_0 and pattern_1) was calculated to decide whether a bit 0 or a bit 1 was embedded. Genetic algorithm was used for embedding and extraction parameters optimization. Optimization is to maximize PSNR of the watermarked image and NCC of the extracted watermark. Experiment results show that the proposed scheme in this paper is robust against many image attacks, and improvement can be observed when compared to other existing schemes

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