Social Federated Computing enables collaborative computation over distributed user-held data in social systems while preserving data locality and participant autonomy. To mitigate gradient inversion-based privacy leakage, intermediate updates are commonly protected through encryption, which fundamentally reduces system observability and weakens conventional poisoning detection mechanisms. This loss of observability introduces new security risks, including ciphertext-level poisoning, while exhaustive cryptographic auditing and verification incur prohibitive computational and communication overheads at scale. These inherent conflicts lie between privacy preservation, detection capability, and operational efficiency in encrypted Social Federated Computing environments. In this paper, we propose an evolutionary game-theoretic defense framework for encrypted Social Federated Computing to model and regulate these inherent conflicts. The framework captures two defining characteristics of social federated environments: adversarial behaviors that propagate along social relationships and organizational constraints, and participants that operate under partial information and exhibit locally rational behavior rather than global rationality. Server-side detection intensity and client-side poisoning propensity are modeled as co-evolving strategies under partial observability, explicitly accounting for false positive penalties, external incentives that motivate poisoning, and practical deployment constraints such as energy budgets. Through equilibrium and stability analysis, we characterize how defense policies adapt to varying costs, incentives, and resource limitations, and we identify stable strategy regimes across representative deployment scenarios. The proposed framework yields deployment-oriented guidance for tuning server-side defenses, enabling adaptive and cost-aware mitigation of ciphertext poisoning in large-scale encrypted Social Federated Computing systems.

Coordinated flooding attacks can significantly reduce the defense response time window in space–ground integrated networks (SGINs), especially when attackers exploit time-varying connectivity through coordinated node-disjoint paths. This paper studies defense response time window optimization from a temporal delay control perspective. We formulate the attacker-defender interaction as a Stackelberg game over a time-varying network, where attackers minimize propagation time and the defender regulates link rates under a global budget to delay attack progression. To address the resulting bilevel problem, we develop two algorithms: DRTW-Opt-E, an exact decomposition-based method, and DRTW-Opt-H, a scalable heuristic combining hybrid genetic search with WTS-aware critical-link approximation. We further identify waiting-time saturation (WTS), which explains the bounded and non-monotonic defense gains in time-varying networks. Experiments on Walker constellations and synthetic graphs show that DRTW-Opt-E solves moderate-scale instances exactly, while DRTW-Opt-H achieves competitive performance with improved scalability. The proposed approach consistently enlarges the defense response time window, with peak gains up to 74

With growing volumes of anomaly traffic and increasingly stealthy attack types, anomaly detection becomes more challenging due to unclear inter-class boundaries and imbalanced intra-class characteristics. To address these issues, we propose DAMA, an effective network traffic anomaly detection framework based on Dimension-Aware Multi-view Alignment. Specifically, our method first partitions network traffic into seven expert-defined dimensions, then utilizes LLMs to generate both flow-level and dimension-level semantic summaries for comprehensive understanding of traffic features. Secondly, we adopt contrastive learning to align network traffic representations between feature space and semantic space, thus enhancing inter-class separability. Finally, dimension-aware prior guidance provides per-dimension supervision by highlighting key features during alignment, which effectively mitigates intra-class bias. Extensive experimental results on two benchmark datasets demonstrate that our proposed method significantly outperforms baselines in both accuracy and F1-score, while providing interpretable insights for traffic anomaly patterns.

Trusted Execution Environments (TEEs) enable secure cross-domain collaborative computation. However, establishing group-oriented secure channels among mutually distrustful enclaves remains challenging, as it must provide mutual authentication while preserving group-level security semantics. To address this, we present GroupTEE, a group-oriented secure communication framework for TEEs. GroupTEE unifies CA-certified identities and remote attestation into a trust-binding mechanism that cryptographically binds group membership and enclave integrity to the derived group session key within a single protocol execution. We prove in the ROM that GroupTEE achieves EUF-CMA authentication, session-key indistinguishability, and key consistency. Experiments show that GroupTEE reduces per-participant attestation generation operations from O(n) in pairwise baselines to O(1) per session.

Personal data are at risk of leakage and unauthorized use during data sharing, while conventional data anonymization techniques may yield excessive information loss. Existing anonymization schemes either face an intrinsic tension between privacy and usability or incur high computational and communication overhead, making them difficult to apply in practical privacy-preserving data-sharing scenarios. To address this challenge, we propose an anonymization framework that achieves data anonymization by replacing sensitive fields with anonymized values while storing the one-way mappings between original and replacement values in encrypted form. To support efficient mapping storage and retrieval, we design the HAS-B tree (Hybrid Nodes and Asynchronous Sorting-based B+ Tree) and its associated algorithms. We further introduce anonymized covering to improve query performance. Experiments on the TPC-H benchmark show that, with anonymized covering, the framework completes queries in only 10

Privacy policies serve as the primary mechanism for companies to disclose their data processing practices. However, vague language in these policies may expose users to risks. Existing methods for evaluating vagueness often struggle to capture fine-grained distinctions in vagueness among terms, lack sufficient consideration for domain-specific terminology, and neglect the phenomenon of false vagueness. To this end, we propose VASH, a VAgueness evaluation method for privacy policies based on the Semantic Hierarchy graph (SHGraph). We first extract terms from privacy policies across 475 industries to ensure comprehensive domain coverage. Leveraging these extracted terms and their hypernym–hyponym relationships, we construct a SHGraph to quantify the vagueness of individual terms based on their position within the semantic hierarchy. Subsequently, we identify false vagueness using the SHGraph and cue words, adjusting term vagueness scores accordingly. Finally, we apply attention weights to capture the influence of terms, thereby deriving sentence-level vagueness scores. To evaluate the effectiveness of VASH, we construct a large-scale dataset of 75,145 privacy policies. Extensive evaluations demonstrate that VASH effectively identifies both vagueness-related terms and false vagueness, yielding results highly consistent with human judgment. Moreover, through the application of VASH to our constructed dataset, we derive four findings that offer insights for enhancing privacy policy clarity. The source code and implementation details are available in our repository [4].

To reduce the performance overhead caused by frequent interval intersection and comparison when conventional interval decision diagrams process interval constraints in access control policies, an efficient policy evaluation and conflict detection method based on half-interval decision diagrams (HIDDs) was proposed. The method uses sequences of half-interval boundaries arranged in ascending order of their right endpoints to implicitly represent attribute-value segments. By recording only the critical points at which policy decisions change, it avoids the redundant storage of complete intervals. Furthermore, a double-pointer scanning-based HIDD composition algorithm is designed to traverse two ordered boundary sequences simultaneously, thereby reducing pairwise interval operations and supporting efficient policy-to-HIDD conversion, access request evaluation, and policy conflict detection. Experimental results show that the construction time of HIDD is up to 72.8% lower than that of conventional interval decision diagrams. Compared with existing approaches, the proposed HIDD method reduces policy evaluation time by up to 48.0% and conflict detection time by up to 55.7%.

Data connectors are key components for enabling controllable data exchange in data marketplaces and constitute one of the foundational technologies for realizing data sovereignty. To support controllable data exchange in lightweight data connectors, it is crucial to design an efficient and scalable access control mechanism. However, user-space access control suffers from high decision latency, whereas kernel-space access control is limited by constrained policy capacity and inflexible policy updates. To address these challenges, we propose an efficient user–kernel access control scheme for lightweight data connectors. The scheme leverages extended Berkeley Packet Filter (eBPF) to load high-frequency access control rules into the kernel while retaining low-frequency rules in user space. To further improve access control efficiency, we design a Bloom-filter-based kernel admission filter to block invalid requests before they enter the policy decision point (PDP). To prevent long-tail requests from polluting the kernel cache, we develop a set-associative, access-frequency-aware cache replacement mechanism. To determine the optimal memory allocation between these two kernel-side components, we model access control decisions as an M/D/1 queueing system, and analyze the relationship between end-to-end policy decision latency and cache miss probability to guide the memory partitioning between the two kernel-side components. We implement a prototype based on eBPF. Experimental results show that, under the same policy set size, our system reduces the average service time by approximately 25 10^6 policies, it reduces the average service time by up to 85.6 γ > 80% , the proposed design reduces latency by up to 65

To address the challenges of verifying the feasibility, correctness, and security of cross-domain data circulation control policies in their generation, transmission, and execution, a formal modeling and verification method was proposed based on timed automata and computation tree logic (CTL). Firstly, the formal models were established for the data circulation control process and key entities in data transaction scene, including data providers, data consumers (encompassing data brokers), and data supervisors. Subsequently, the security requirements and circulation control properties were formalized during data transactions using CTL specifications. Finally, the aforementioned timed automaton model was simulated, with formal verification and analysis performed on its behavioral properties and structural attributes. The proposed method can effectively validate the feasibility, correctness, and security of data circulation control mechanisms.

Hardware Trojans pose a significant security risk in space-ground integrated network (SGIN) devices. It is widely accepted in academia and industry that detecting hardware Trojans at an early stage, typically in register transfer-level (RTL) hardware design, can effectively protect the SGIN device. However, the few hardware Trojan samples dedicated to SGIN (called sHT) make it difficult to detect them using deep learning. To obtain more sHT samples automatically and quickly, this article proposes a lightweight automatic sample augmentation framework for hardware Trojan (HT-ASAF). In our scheme, we first designed a lightweight neural network called variational autoencoder for hardware Trojan (HT-VAE) to achieve high-generation quality without a large amount of training data. Further, we develop the positional state tree (PST) and introduce a node tuple representation for interconversion between PST and sequence to capture the intricate semantic features of concurrent operations in hardware design to enhance the performance of HT-VAE. To automatically verify the effectiveness of the augmented samples, we established an experimental platform incorporating cluster mapping (CLM), which can reduce the verification complexity. In our experiments, to obtain a small number of the training hardware Trojan samples for SGIN, we added activation mechanisms, such as velocity or altitude, to the existing RTL hardware Trojans samples to simulate the hardware Trojan threats faced by orbit devices. The set of the obtained samples is called sHT dataset. Experimental results on the obtained sHT dataset demonstrate that HT-ASAF can automatically and efficiently augment hardware trojan sample compared to existing augmentation schemes, and it performs well in the downstream task of hardware Trojan detection on SGIN devices.

Satellite computing systems (SCSs), with their enormous economic value, are suffering from increasing attacks. Moving Target Defense (MTD), which changes the attack surface to create an asymmetric situation between attacks and defenses, can be used to improve the safety of the SCS. Defense timing selection is crucial for enhancing the defense capability of MTD and reducing its cost. However, existing MTD defense timing selection strategies do not consider limited defense resources and periodic user traffic in the SCS, which leads to significant resource consumption and impacts a large volume of traffic, making them unsuitable for the SCS. We propose a Markov Game-based Defense Timing Selection (MGDTS) approach to protect the SCS. We divide the orbital cycle of the SCS into several time periods with different traffic rates. For each period, we formulate the attack-defense adversarial relationship as a Markov game with incomplete information. In the game, we use explicit costs to define the resource consumption of a defender. Further, we employ Markov decision processes to construct the defense timing decision equation and use real-time dynamic programming to solve the equation. Experimental results show that compared with the existing MTDs, our scheme can enhance security while reducing resource consumption and the influence on user traffic. This work is an extended version of the ICCS-2024 conference paper (Lin Zhang et al., 2024).

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.

Enterprises face security threats from internal anomalous behaviors in dynamic network environments. Existing rule-generation approaches often rely heavily on large amounts of labeled data and are particularly sensitive to data scarcity. In Small and Medium-sized Enterprises (SMEs), limited resources and the imbalance between positive and negative samples make it difficult to obtain sufficient labeled data for detecting anomalous behaviors. This challenge often leads to poor detection performance. To address this issue, this paper proposes an adaptive behavior rule-generation approach that combines Chain-of-Thought (CoT) reasoning and few-shot learning, eliminating the need for large-scale labeled data. In this approach, we employ CoT to perform step-by-step reasoning on user behavior, generating personalized anomalous behavior detection rules based on limited data. To enhance the adaptability and accuracy of the approach, we integrated few-shot learning, enabling the system to learn from a small number of examples and generate effective rules. Experimental results demonstrate that the proposed method achieved an accuracy of 97.56

Geospatial data constitutes a critical strategic resource and an emerging factor of production. This data encompasses geographic entities about the entire Earth's surface, capturing detailed features and properties. Through coordinate systems, it achieves precise spatial positioning while revealing dynamic temporal variations. Since it integrates extensive spatial coverage with rich attribute records, this data often reaches large volumes. Existing access control models and mechanisms, however, struggle to regulate how this data is used as it circulates through governance and application scenes. To tackle these challenges in the complex network environment of the real-time intelligent service (PNTRC) network for cross-domain geospatial data, a Geospatial Data Circulation Control (GDCC) model and administrative model were developed, based on the Cyberspace Oriented Access Control (CoAC) model. It provides tailored implementation mechanisms for geospatial data circulation control in various scenes and formally defines and analyzes its functions and methods using Z language. Analysis and evaluation demonstrate that the proposed circulation control mechanism effectively ensures controlled transmission, usage and extended control throughout the entire lifecycle of cross-domain geospatial data circulation.

Federated learning is prone to privacy leakage issues. Current research on secure aggregation protocols can protect user privacy during the federated learning process. However, as the number of participants and the volume of data increase, the communication overhead and computation time grow polynomially. Therefore, traditional secure aggregation protocols are challenging to apply in resource-constrained environments. Recent works have customized secure aggregation protocols based on Rand-K and Top-K sparsification, which have significantly reduced communication and computation overhead. Nevertheless, there is still substantial room for improvement. This paper builds on Top-K sparsification by integrating quantization and customizing a secure aggregation protocol SecSQ which is based on homomorphic encryption. This approach further reduces the amount of encryption and decryption operations, thereby achieving lower communication and computation costs. Additionally, this paper designs an oblivious random grouping protocol that randomizes group assignments and designates temporary clients for aggregation in each iteration. This creates a dynamically changing pyramid structure within the federated learning framework, which not only protects user privacy but also distributes the server’s computational and communication load. Experiments demonstrate that the proposed method reduces global communication volume by 70

Space-Ground Integrated Networks (SGIN) devices are at risk of hardware Trojan attacks. Currently, existing detection schemes (e.g., deep learning) require a large amount of labeled samples. However, obtaining a high-quality labeled hardware Trojan dataset for SGIN devices is challenging due to the structural complexity of hardware, resulting in poor detection performance. To address this challenge, this paper combines prompt-tuning with LangChain to propose a hardware Trojan detection scheme for SGIN devices without requiring extensive training samples. In our scheme, we transform hardware Trojan detection into a mask prediction problem and design a two-phase prompt-based detection framework. In the first phase, we design 5 prompt patterns with masks and utilize Roberta-large as a large language model (LLM) to predict masks and their confidence. If their confidence is below a given threshold value, the second phase is initiated, where the corresponding original samples are fed into LangChain to optimize detection. To enhance the detection accuracy, we develop a Positional State Tree (PST) to extract the logical parallel structure of SGIN Trojan. Experiments show that our scheme achieves an accuracy of 91.3% in detecting the presence of Trojans and 96.3% in identifying the types of Trojans, respectively.

Access control policy mining, which extracts or infers access control policies from existing system logs or configurations within a given environment, is widely applied in policy migration. No policy mining algorithm can produce completely accurate policies, necessitating human intervention to correct errors before implementation. However, humans may fail to revise the produced policies without fully understanding all permission details. In this paper, we propose an on-the-fly framework to assist human revisions by mining usable policies. This framework is formulated as an approximate optimization problem, designed to avoid permission errors and redundancy by balancing submodularity and modular costs through an iterative search for access rules. The search space is guided by two pruning techniques: a tight optimistic estimate to only eliminate unpromising candidates and a queue cutter to sample promising candidates in advance. Experimental evaluations on two real-world and three publicly available synthetic datasets indicate that: 1) our method produces more concise results than existing methods, achieving a 93.2% reduction in redundancy; 2) our method is at least five times faster than state-of-the-art approaches. To further validate the usability of the policies obtained by our approach, we conducted a user study involving 30 participants and 7 large language models (LLMs). The results show that 90.9% of participants, including LLMs such as gpt-4o-mini, successfully modified our mined policies to meet given permission goals.

The Satellite Computing System (SCS) faces an increasing number of attacks. Although Moving Target Defense (MTD) can effectively mitigate attacks in ground networks, it is not well-suited for SCS due to the highly dynamic nature of both SCS traffic and attackers’ scanning behaviors. In this paper, we propose a dynamic MTD strategy optimization scheme using Advantage Actor-Critic (A2C) reinforcement learning. Specifically, we formulate the MTD strategy optimization for SCS as a Markov Decision Process (MDP). Furthermore, by accounting for the uncertainty in attack behavior changes, we apply A2C reinforcement learning to optimize the MTD strategy within the MDP framework. Experimental results demonstrate that our scheme effectively reduces the frequency of scanning hits, shortens the duration attackers can hold addresses, and minimizes the impact of MTD on quality of service.