In end-to-end data sharing, data are directly distributed to data receivers and stored on their terminals, making it hard to ensure forward security because receivers whose permissions have been revoked may still access previously shared data. To address these challenges, we propose an attribute-key-free CP-ABE scheme, aimed at securely binding data with access policies while ensuring forward security. Specifically, the decryption process in our scheme is delegated to the attribute authorities, which adopt the user’s real-time attribute values to decrypt the ciphertext. To prevent the honest-but-curious attribute authorities from accessing the plaintext, the ciphertext is re-encrypted with a one-time key before being sent to the attribute authorities. Furthermore, to prevent sensitive information from being inferred through the policy, we design a policy-hiding mechanism to conceal attribute values. Through these mechanisms, it can be ensured that the data subject always has control over his or her personal data during the end-to-end data-sharing process. We evaluate the performance of our scheme through both theoretical analysis and comparative experiments, and the results show our scheme’s effectiveness.

Graph anomaly detection (GAD) aims to identify nodes, edges, or subgraphs that significantly deviate from normal patterns in graph data, holding critical application value in domains such as social network security and financial fraud detection. Due to the extreme scarcity of anomaly labels in real-world scenarios and prohibitively high annotation costs, developing unsupervised detection algorithms without labeled priors constitutes a core research challenge. Current mainstream approaches can be categorized into reconstruction-based and contrastive learning-based methods. The former typically suffers from single-granularity reconstruction limitations, failing to capture high-order semantic information while incurring substantial computational overhead. The latter excessively relies on local neighborhood structures to construct contrastive pairs, overlooking the effective utilization of macroscopic global information. Therefore, this paper proposes a Hybrid Multi-granularity Reconstruction and Contrastive Learning method for Graph Anomaly Detection. Within the reconstruction module, we design dual-granularity tasks, node self-reconstruction and multi-hop neighborhood reconstruction, in a low-dimensional latent space. The contrastive module employs an attention mechanism-based global graph feature transformation to construct semantically consistent sample pairs. By jointly optimizing both tasks, our approach adaptively fuses local and global anomaly signals to accommodate diverse anomaly patterns. Extensive experiments on five real-world datasets demonstrate that our method significantly outperforms current state-of-the-art benchmarks.

Access control policies play a critical role in securing sensitive data and protecting personal rights in environments such as cloud computing and IoT. These policies, typically created by sysadmins, specify which users are authorized to access specific resources under certain conditions. However, the manual creation and revision of these policies to align with security objectives is often error-prone and labor-intensive. In this paper, we present Custominer, a policy mining tool designed to assist sysadmins in proactively generating and customizing access control policies that meet predefined security requirements. Custominer enables sysadmins to define security goals as constraints, and then automatically mines policies that satisfy these constraints from access logs. The policy mining task is framed as a local search optimization problem, utilizing a MaxSAT solver to efficiently eliminate suboptimal policy candidates. Our experiments, conducted on four real-world datasets, show that Custominer outperforms existing state-of-the-art methods in terms of both accuracy and efficiency.

Insiders cause serious security threats to organizations. Existing insider threat detection methods mainly mine the users' behaviors or psychological features by analyzing the users' operation logs, and they ignore the associations of behaviors among users and get unappealing performance on the imbalanced samples. In this paper, considering attention mechanism, we propose Generative Adversarial Graph Attention Networks (GAGAN) to detect insider threats. First, we design association rules to construct a graph to associate users' behaviors. Second, to address the imbalanced samples, we adopt graph generator to generate abnormal nodes; A discriminator with graph attention networks is designed to further mine the potential associations of behaviors among users and discriminate real nodes from the generated nodes, also adopted to discriminate anomaly nodes from normal nodes. Experimental results on CERT data set demonstrate that our method can accurately detect abnormal insiders and outperforms several state-of-the-art baseline methods.

To realize data sovereignty, the International Data Space (IDS), adopting usage policies to determine how, when and where other enterprises or individuals may use data, has been proposed by the IDS association and widely received attention from academia and industry. However, because data in the IDS are transferred across domains, existing policy creation approaches for a single domain cannot be applied in the IDS. To address this problem, in this paper, we propose a negotiation scheme to create usage policies in the IDS. In detail, we formulate usage policy negotiation as a combinatorial auction problem and adopt the Vickrey-Clarke-Groves (VCG) mechanism to incentivize potential data providers to truthfully negotiate usage policies. Both theoretical and simulation results show that our scheme maintains truthfulness on data providers and is cost-efficient.