The fault location task in power grids is crucial for maintaining social order and ensuring public safety. However, existing methods that rely on tabular state records often neglect the intrinsic topological influences of transmission lines, resulting in a segmented approach to fault location that consists of multiple stages. In this paper, we propose an Disentangled Table-Graph representation framework, termed DTG, which integrates fault location tasks at coarse-grained line levels and fine-grained point levels within an end-to-end learning paradigm. Our innovative disentanglement strategy produces interpretable attribution coefficients that connect tabular records and transmission line topology, thereby facilitating fault location at both line- and point-levels. The joint prediction tasks designed around our disentangled tabular graph representation promote mutual information exchange between features and topology of transmission lines in an interpretable manner. Experimental results on the 7-bus system, 36-bus system and a realistic 325-bus system in China demonstrate that the proposed method adapt to different topological structures and handle different types of faults. Compared to traditional methods, DTG4Power achieves high accuracy in both fault lines and fault points.

As an emerging class of internet of vehicles, vehicular social networks (VSNs) provide passengers, drivers, and vehicles with extensive data sharing services to improve traffic congestion and road safety. However, the insecure transmissions of shared data may disclose sensitive information, such as private data, location, and driving route. Although attribute-based encryption (ABE) is a promising technology to enable secure data sharing, the existing ABE solutions applied to VSNs encounter three-fold deficiencies: (1) the shared data stored in vehicular cloud server would be leaked in the event of key compromise; (2) relying on one or more fully trusted entities to generate keys for vehicles through secure channels; (3) private information leakage and misbehavior of data user vehicles are neglected. Motivated by these challenges, this paper proposes a puncturable registered ABE scheme called PR-ABE for VSNs with enhanced security and practicality. To be specific, our PR-ABE achieves flexible access control and precise data deletion. The former ensures that only registered vehicles with authorized attributes can obtain the shared data. The latter prevents data disclosure when key compromise happens. Meanwhile, PR-ABE enables vehicles to generate keys independently and eliminates the need for any fully trusted authority. In addition, hidden policy and traceability are fulfilled in PR-ABE to protect private information and deal with malicious vehicles, respectively. Finally, the rigorous security proof and performance evaluation demonstrate that PR-ABE is a practical and efficient solution.

The diverse properties of wireless networks are fulfilled with the assistance of digital twin (DT), which utilizes a virtual model of the physical object (PO) to provide predictions and control decisions. However, the open wireless channels and key leakage of compromised entities (including DT and PO) pose significant security issues, highlighting the need for secure data transmission schemes. Meanwhile, it is impractical to directly apply the existing works and cryptographic primitives to DT-empowered wireless networks (DTWNs) due to the absence of a solution to capture the security requirements comprehensively. Moreover, the essential characteristics for protecting historical data cannot be met. Therefore, this paper proposes a security-enhanced data transmission scheme with fine-grained and flexible revocation by customizing a novel cryptographic primitive named forward-secure puncturable signed encryption (FS-PSE). Our scheme enables confidential data dissemination/acquisition between the physical and virtual space while ensuring authentication of the real-time information and feedback results. In addition, three revocation modes are defined. Based on these modes, the entities can flexibly revoke any decryption-&-signature, decryption, and signature capability in a fine-grained approach, thereby providing security protections for the historically transmitted data even though the entity is compromised. Moreover, our scheme is instantiated with a concrete FS-PSE construction and extended to support outsourced computing to improve efficiency. Finally, the formal security proof and performance evaluation demonstrate the security and practicality of our scheme.

In transportation 5.0, digital twin (DT) is considered a promising paradigm to integrate physical entities into cyber physical systems by collecting massive data. However, the open collection process and key exposure issues bring critical security challenges. Furthermore, applying the existing authentication schemes to data collection in transportation DT (TDT) systems encounters three deficiencies: 1) forward security for collected data can only be achieved at a coarse-grained level; 2) one or more additional trusted authorities are introduced, causing the robustness of TDT systems to be downgraded; 3) dynamic attribute updating and revocability of physical entities are rarely considered. Therefore, we propose a dual fine-grained authentication scheme (DFAS) in this paper. Our DFAS can not only ensure data integrity and authenticity but also enable fine-grained access control, namely, only registered physical entities with authorized attributes can generate valid signatures. Meanwhile, DFAS provides the key puncturing for physical entities to guarantee fine-grained forward security without relying on any trusted authority. In addition, a non-interactive attribute updating and revocation of malicious entities are realized in DFAS. Finally, the security analysis indicates that DFAS can deal with various security challenges for data collection in TDT systems. The performance evaluation demonstrates that DFAS is efficient and practical.

Graph data is becoming dynamic and large-scale, demanding high-performance and large-capacity graph storage. Therefore, due to the performance approaching DRAM and the larger capacity than DRAM, persistent memory (PM) has been adopted in large-scale dynamic graph storage systems. However, existing PM-based dynamic graph storage systems have issues, especially PM write amplification caused by the unsorted data structure used to store edges. To improve this issue, we propose a PM-based dynamic graph storage system, HDGraph, using sorted data structure to store edges on DRAM-PM hybrid memory architecture. To better adapt the sorted data structure on PM, HDGraph employs edge buffering on DRAM, merging small writes to reduce write amplification in PM. Moreover, HDGraph also triggers buffer flushing based on a heat evaluating strategy to alleviate DRAM space pressure. Finally, HDGraph maintains a buffering log in PM for edge-level data consistency, enabling quick recovery after a crash. Experimental results show that HDGraph achieves 1.09× to 1.52× higher edge ingestion performance, compared with the only PM-based dynamic graph storage system XPGraph, which use unsorted data structure to store edges.

Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in recent years, revealing the suspiciousness of nodes by aggregating their neighborhood information via different relations. However, few prior works have noticed the camouflage behavior of fraudsters, which could hamper the performance of GNN-based fraud detectors during the aggregation process. In this paper, we introduce two types of camouflages based on recent empirical studies, i.e., the feature camouflage and the relation camouflage. Existing GNNs have not addressed these two camouflages, which results in their poor performance in fraud detection problems. Alternatively, we propose a new model named CAmouflage-REsistant GNN (CARE-GNN), to enhance the GNN aggregation process with three unique modules against camouflages. Concretely, we first devise a label-aware similarity measure to find informative neighboring nodes. Then, we leverage reinforcement learning (RL) to find the optimal amounts of neighbors to be selected. Finally, the selected neighbors across different relations are aggregated together. Comprehensive experiments on two real-world fraud datasets demonstrate the effectiveness of the RL algorithm. The proposed CARE-GNN also outperforms state-of-the-art GNNs and GNN-based fraud detectors. We integrate all GNN-based fraud detectors as an opensource toolbox https://github.com/safe-graph/DGFraud. The CARE-GNN code and datasets are available at https://github.com/YingtongDou/CARE-GNN.

Graph-based models have been widely used to fraud detection tasks. Owing to the development of Graph Neural Networks~(GNNs), recent works have proposed many GNN-based fraud detectors based on either homogeneous or heterogeneous graphs. These works leverage existing GNNs and aggregate the neighborhood information to learn the node embeddings, which relies on the assumption that the neighbors share similar context, features, and relations. However, the inconsistency problem incurred by fraudsters is hardly investigated, i.e., the context inconsistency, feature inconsistency, and relation inconsistency. In this paper, we introduce these inconsistencies and design a new GNN framework, GraphConsis, to tackle the inconsistency problem: (1) for the context inconsistency, we propose to combine the context embeddings with node features; (2) for the feature inconsistency, we design a consistency score to filter the inconsistent neighbors and generate corresponding sampling probability; (3) for the relation inconsistency, we learn the relation attention weights associated with the sampled nodes. Empirical analysis on four datasets demonstrates that the inconsistency problem is critical in fraud detection tasks. Extensive experiments show the effectiveness of GraphConsis. We also released a GNN-based fraud detection toolbox with implementations of SOTA models. The code is available at \urlhttps://github.com/safe-graph/DGFraud