With the tremendous growth of cloud computing, verifiable computation has been firstly formalized by Gennaro et al. and then studied widely to provide integrity guarantees in the outsourced computation. However, existing verifiable computation protocols either work in the secret key setting or in the public key setting, namely, work either for single client or for all clients, which rules out some practical applications with access control policies. In this paper, we introduce and formalize the notion of verifiable computation with access control (AC-VC), in which only the computationally weak clients with necessary access control permissions can be allowed by a trusted source to apply the outsourced computation of a function to a server. We present a formal security definition and a proved secure black-box construction for AC-VC. This construction is built based on any verifiable computation in the secret key model and ciphertext-policy attribute-based encryption (CP-ABE). The access control policies that our AC-VC can realize depend on that realized in the based CP-ABE. 相似文献
Classic kernel principal component analysis (KPCA) is less computationally efficient when extracting features from large data sets. In this paper, we propose an algorithm, that is, efficient KPCA (EKPCA), that enhances the computational efficiency of KPCA by using a linear combination of a small portion of training samples, referred to as basic patterns, to approximately express the KPCA feature extractor, that is, the eigenvector of the covariance matrix in the feature extraction. We show that the feature correlation (i.e., the correlation between different feature components) can be evaluated by the cosine distance between the kernel vectors, which are the column vectors in the kernel matrix. The proposed algorithm can be easily implemented. It first uses feature correlation evaluation to determine the basic patterns and then uses these to reconstruct the KPCA model, perform feature extraction, and classify the test samples. Since there are usually many fewer basic patterns than training samples, EKPCA feature extraction is much more computationally efficient than that of KPCA. Experimental results on several benchmark data sets show that EKPCA is much faster than KPCA while achieving similar classification performance. 相似文献
Summary: Compacted fiber composites offer unique properties due to their lack of an extraneous matrix. The conditions of processing ultra‐high molecular weight polyethylene (UHMWPE) fibers were simulated in a heated pressure cell. In situ X‐ray diffraction measurements were used to follow the relevant transitions and the changes in the degree of crystallinity during melting and crystallization. The results strongly support the suggestion that the hexagonal crystal phase, in which the chain conformation is extremely mobile on the segmental level, constitutes the physical basis of compaction technologies for processing UHMWPE fibers into a single‐polymer composite. This report suggests that using a pseudo‐phase diagram outlining the occurrence of different phases during slow heating and the degree of crystallinity can provide valuable insight into the technological parameters relevant for optimal processing conditions.
Degree of crystallinity as a function of pressure and temperature in a region relevant to compaction processes. 相似文献