Multi-view clustering is a fundamental unsupervised learning paradigm designed to partition multi-view data into disjoint clusters. In real-world scenarios, however, multi-view data are often distributed across various devices, institutions, or edge nodes, motivating the development of federated multi-view clustering. Within this decentralized setting, issues such as missing views, privacy constraints, and high communication overhead pose significant challenges. To address these limitations, we propose a novel framework termed Federated Incomplete Multi-View Graph Clustering via View Completion (FMVC-VC). Specifically, FMVC-VC seamlessly integrates client-side missing-view recovery with server-side global structure optimization. By leveraging view-specific anchor graphs as lightweight knowledge carriers, the framework avoids direct feature-level exposure while substantially reducing communication costs. At the client level, global structural information from the server guides the reliable imputation of missing-view features. Concurrently, at the server level, we introduce a contrastive learning mechanism to refine cross-client structural consistency and further enhance the discriminative capacity of the global anchor graph. Extensive experiments on multiple benchmark datasets demonstrate that FMVC-VC significantly outperforms current federated multi-view clustering methods, particularly in challenging incomplete-view scenarios.
Industrial control and cyber–physical systems require security mechanisms that are both verifiable by design and analyzable after deployment. This paper introduces a dual-entry Petri net–based methodology that combines design-time modeling via verified secure primitives with post hoc analysis driven by MITRE ATT CK techniques and CIA-oriented vulnerability motifs. The framework captures adversarial behaviors and defensive mechanisms as composable Petri net fragments, and formalizes CIA impact via violation semantics over reachable markings, enabling both proactive and diagnostic security reasoning. We define canonical constructs for confidentiality, integrity, and availability, providing reusable templates for modeling vulnerabilities and countermeasures. Through illustrative case studies (including secure firmware updates and attack-path analysis in industrial automation scenarios), we show how the proposed framework supports systematic vulnerability discovery, model-based verification, and the development of reusable Secure Primitive Functions for design-time hardening.
High-throughput steganographic coding remains difficult because syndrome-trellis codes (STC) incur high computational cost at practical security settings, while steganographic polar codes retain strong serial dependencies in their successive decoding schedule. This paper proposes CuSPC, a GPU-oriented steganographic polar coding architecture that restructures simplified successive cancellation (SSC) execution, introduces an ordinary-node (Ord) design for short residual subtrees, and combines inter-cover parallelism with interleaved global-memory access. The implementation maps intra-node, inter-node, and inter-cover parallelism to the CUDA execution model while preserving the distortion-minimizing formulation of polar steganographic coding. Experiments show that CuSPC reaches up to approximately 39 Mb/s across the tested devices and improves throughput by about 30 times over the CPU serial baseline, while keeping the coding loss below 2
Deep neural networks have achieved remarkable success in image classification, but their training processes remain vulnerable to backdoor attacks. Existing frequency-domain backdoor attacks typically rely on manually designed and data-independent triggers, limiting their adaptability across different models and datasets. Moreover, fixed spectral perturbations may introduce detectable frequency anomalies. To address these limitations, we propose CLTBA, a backdoor attack based on learnable frequency-domain triggers. CLTBA jointly optimizes the trigger distribution and model parameters through an adaptive spectral mask and a dynamic training strategy, enabling flexible trigger generation while preserving visual quality. A spectral preservation mechanism further constrains trigger perturbations within a controllable frequency region, thereby reducing spectral anomalies and improving stealthiness. Experiments on GTSRB, CelebA, and MNIST with ResNet-18 and EfficientNet-B0 demonstrate that CLTBA achieves high attack success rates while maintaining benign accuracy and shows improved resistance to representative backdoor defenses and analysis methods, including STRIP, Neural Cleanse, Fine-Pruning, and Grad-CAM.
In AIoT service scenarios such as sensing-as-a-service and edge AI outsourcing, a service requester may need to establish a payment commitment before service execution, while final transaction should be deferred until service delivery is verified. When the requester and the provider hold assets on different blockchains, this requires a cross-chain deferred asset transfer. Existing cross-chain protocols focus on immediate transaction. Adaptor-signature-based swaps assume that once a linked transfer completes, the secret becomes immediately usable; deferred transaction requires the adaptor secret to remain unavailable even after the binding has been established. We propose a scriptless cross-chain deferred asset transfer protocol based on a delayed timed atomic lock (DTAL) construction. DTAL introduces a delayed adaptor secret mechanism that preserves atomic binding between two cross-chain transfers while preventing the secret from being used before a predefined release time. It is instantiated using two-party adaptor signatures, time-release encryption, and verifiable timed discrete logarithm. The underlying blockchains only need to validate ordinary transactions and signatures. Security analysis shows that the proposed protocol achieves atomicity and timeout safety. Experimental results demonstrate low running time and communication overhead while reducing gas costs compared with contract-based cross-chain approaches.
