It is secure for customers to store and share their sensitive data in the cryptographic cloud storage. However, the revocation operation is a sure performance killer in the cryptographic access control system. To optimize the revocation procedure, we present a new efficient revocation scheme which is efficient, secure, and unassisted. In this scheme, the original data are first divided into a number of slices, and then published to the cloud storage. When a revocation occurs, the data owner needs only to retrieve one slice, and re-encrypt and re-publish it. Thus, the revocation process is accelerated by affecting only one slice instead of the whole data. We have applied the efficient revocation scheme to the ciphertext-policy attribute-based encryption (CP-ABE) based cryptographic cloud storage. The security analysis shows that our scheme is computationally secure. The theoretically evaluated and experimentally measured performance results show that the efficient revocation scheme can reduce the data owner’s workload if the revocation occurs frequently.

Cloud computing bring a tremendous complexity to information security. Remote attestation can be used to establish trust relationship in cloud. TBVMM is designed to extend the existing chain of trust into the software layers to support dynamic remote attestation for cloud computing. TBVMM uses Bayesian network and Kalman filter to solve the dynamicity of the trusted relationship. It is proposed to fill the trust gap between the infrastructure and upper software stacks.

The goal of multi-view graph clustering (MVGC) for remote sensing data is to obtain a consistent partitioning by capturing complementary and consensus information across multiple views. However, numerous ambiguous background samples in multi-view remote sensing data increase structural heterogeneity while simultaneously hindering effective information extraction and fusion. Existing MVGC methods cannot selectively integrate and fully refine both graph structure and node attribute information for consensus representation learning. Furthermore, current methods tend to overlook distant nodes, thus failing to capture the global graph structure. To solve these issues, we propose a novel method called Improved Multi-View Graph Clustering with Global Graph Refinement (IMGCGGR). Specifically, we first design a view-specific fusion network (VSFN) to extract and integrate node attribute and structural information into view-specific representation for each view. VSFN not only utilizes a global self-attention mechanism to enhance the global properties of structural information but also constructs a clustering loss through a self-supervised strategy to guide the view-specific clustering distribution assignment. Moreover, to enhance the capability of view-specific representation, a learnable attention-driven aggregation strategy is introduced to flexibly fuse the attribute and structural feature. Then, we adopt a cross-view fusion module to adaptively merge multiple view-specific representations for generating the final consensus representation. Comprehensive experiments show that IMGCGGR achieves significant clustering performance improvements over baseline methods across various benchmark datasets.

Existing multi-view clustering approaches based on matrix factorization often fail to jointly capture global high-order correlations and local view-specific characteristics, and they typically suffer from instability in generating final clustering labels. To overcome these limitations, this paper presents a multi-view subspace clustering method termed dual-tensor constrained multi-view subspace clustering (DTCMVSC). Specifically, for each view, we learn an independent latent representation matrix, a projection matrix, and a basis matrix. The latent representations and projection matrices are stacked into third-order tensors, upon which tensor nuclear norm regularization is imposed to simultaneously exploit consensus structures and complementary information across views. Additionally, a consensus regularization term and adaptive view weights are introduced to align the latent representations of different views toward a unified consensus subspace. The resulting optimization problem is efficiently solved under the ADMM framework, after which a similarity matrix is constructed from the consensus representation and spectral clustering is performed to obtain the final labels. Experimental evaluations on six benchmark datasets demonstrate the superiority of DTCMVSC. Specifically, it achieves an ACC of 86.10% on CMU and an NMI of 94.17% on ORL, surpassing even the lowest-performing state-of-the-art baselines by 63.08 and 18.53 percentage points, respectively.

A clustering ensemble, which leverages multiple base clusterings to obtain a reliable consensus result, is a critical challenging task for Earth observation in remote sensing applications. With the development of multi-source remote sensing data, exploring the underlying graph-structured patterns has become increasingly important. However, existing clustering ensemble methods mostly employ shallow clustering in the base clustering generation stage, which fails to utilize the structural information. Moreover, the high dimensionality inherent in data further increases the difficulty of clustering. To address these problems, we propose a novel method termed Global Self-Attention-driven Graph Clustering Ensemble (GSAGCE). Specifically, GSAGCE firstly adopts basic autoencoders and global self-attention graph autoencoders (GSAGAEs) to extract node attribute information and structural information, respectively. GSAGAEs not only enhance structural information in the embedding but also have the capability to capture long-range vertex dependencies. Next, we employ a fusion strategy to adaptively fuse this dual information by considering the importance of nodes through an attention mechanism. Furthermore, we design a self-supervised strategy to adjust the clustering distribution, which integrates the attribute and structural embeddings as more reliable guidance to produce base clusterings. In the ensemble strategy, we devise a double-weighted graph partitioning consensus function that simultaneously considers both global and local diversity within the base clusterings to enhance the consensus performance. Extensive experiments on benchmark datasets demonstrate the superiority of GSAGCE compared to other state-of-the-art methods.