Abstract Industry 4.0 leverages information and intelligent technologies to create a highly flexible production model that supports personalized and digital products and services. However, stringent privacy protection requirements have resulted in the fragmentation and inaccessibility of production data across various companies, creating a significant data barrier. Federated learning (FL) effectively addresses privacy concerns during the data sharing process and facilitates the collaborative use of industrial production data across various institutions. However, due to the characteristics of the FL training model, attackers can implement reconstruction attacks and attribute inference attacks on the local model or global model to obtain the privacy of the data provider. Additionally, poisoning attacks by malicious users can damage the performance of FL models, thereby affecting the system’s overall utility. Many studies propose new techniques that combine privacy mechanisms and poisoning detection, but most solutions rely on outlier detection and introduce additional risks, making them impractical for real-world applications. To address these issues, we propose a privacy-preserving FL optimization method capable of resisting highly concealed poisoning attacks, FedCPA. Specifically, we protect the local model by adding eliminable noise, allowing the server to perform secure aggregation based on the fuzzy model. Furthermore, we design a ciphertext-based, two-layer architecture poisoning model detection algorithm to defend against highly concealed poisoning attacks such as Sybil attacks. Finally, the correctness of the entire calculation process is calculated and proven.

Acquiring synthetic aperture radar (SAR) images is inherently difficult and laborious. To mitigate this data scarcity, generative models for SAR images aim to learn underlying data distributions from large-scale datasets, ensuring robust performance. Generative models mitigate data scarcity but rely on centralized frameworks, posing privacy risks and limiting deployment in Internet of Things (IoT) scenarios. To tackle the aforementioned challenges, we introduce an innovative federated angle-aware generative diffusion (FAGD) framework for secure SAR image generation. This framework integrates three key innovations: federated learning (FL), the angle-aware generative diffusion (AAGDiff) model, and the local data knowledge distillation (LDKD) strategy. Specifically, we introduce the FL framework, which enables clients to collaboratively train a generative model by transmitting model weights, thereby eliminating raw data exchange and enhancing security. We then propose the AAGDiff model for client-side high-quality generation, leveraging denoising diffusion probabilistic models (DDPMs) to synthesize high-quality SAR images from noise using an angle-conditioned encoder, enabling the generation of SAR images at different target azimuth angles. Additionally, the LDKD strategy alleviates overfitting during federated training under Non-Independent and Non-Identically Distributed (non-IID) data distributions by allowing the model to distill knowledge primarily from less frequent classes, thus enhancing generalization and robustness. Evaluated extensively on MSTAR and OpenSARShip datasets, the proposed framework ensures secure SAR image generation while achieving superior target recognition performance. Overall, the FAGD framework offers an effective and scalable solution for privacy-preserving SAR image synthesis.

With the growth of cloud computing infrastructure, its cost-efficient paradigm is driving a growing wave of small and medium-sized enterprises to migrate data and services to cloud based platforms. However, due to privacy concerns, data encryption prior to outsourcing is an important means of protection, which in turn requires performing queries over the encrypted data. While several approaches do offer support for privacy preserving skyline or top-k queries, they typically struggle with efficiency when extended to secure top-k dominating queries, due to the inherent nature of combining the advantages of both top k and skyline queries. Nevertheless, this nature renders them as a more practical and promising alternative for location-based services. To address this, we introduce STLD, a secure top-k location-based dominating query scheme. Specifically, we develop an innovative index structure called Secure Aggregate R-tree (SAR-tree) by utilizing the Paillier cryptosystem and introducing meticulously crafted noise, while also incorporating principles from aggregate R-trees and semi-blind R-trees. Leveraging this structure, we propose a series of secure sub-protocols to facilitate top-k dominating queries, accompanied by optimization techniques to mitigate latency associated with computationally intensive dominating operations. Given an encrypted query, STLD not only efficiently answers the query but also guarantees the privacy of data(sets), results, queries and access patterns. Finally, STLD undergoes rigorous theoretical security and complexity analysis, complemented by empirical evaluations that demonstrate its performance and feasibility, achieving a reduction in query cost by 40%-60% compared to multiple competing methods.

Privacy-preserving federated learning (PPFL) is a strong-privacy distributed learning paradigm, which typically employs secure aggregation (SA) protocols to protect the aggregation results of federated learning. However, the high computational cost of existing SA protocols is difficult to generalize to decentralized federated learning (DFL) with the large number of clients and fails to defend against model poisoning attacks launched by Byzantine adversaries. In this paper, we propose an efficient computational SA protocol compatible with DFL, referred to as PriDFL, and address the issue of Byzantine-robust aggregation. Specifically, we design an advanced secret-sharing protocol based on number-theoretic transformations to reduce the computational complexity from$O(n^{2})$to$\mathcal {O}(n\log n)$during data sharing. We employ a single-mask approach to provide lightweight gradient privacy protection for DFL. To mitigate the impact of poisoned gradients on model convergence, we develop a Byzantine resilience criterion grounded in model cross-updating. The proposed criterion efficiently detects poisoned gradients and non-independent identically distributed (non-IID) data with local computation. Security analysis shows that PriDFL satisfies the security requirements in an honest but curious setting. We evaluate PriDFL on typical datasets (e.g., MNIST and CIFAR-10) and the results show that PriDFL is computation-communication efficient and Byzantine resilient. In particular, PriDFL optimizes the computational efficiency by 6-10× compared to the well-utilized SA protocols while supporting Byzantine robustness.

Federated learning (FL) allows multiple distributed clients with local datasets to train a global model collaboratively. Due to the potential privacy risk of the training process, differential privacy (DP) is introduced into FL to protect clients’ sensitive information by perturbing the model updates. However, the probability density function of the Laplace mechanism has a long-tail effect, which may generate large noise to induce the model to deviate from the normal result. Moreover, as the cloud is not fully trusted, there is no guarantee that the server follows the aggregation protocol correctly. To address these issues, in this paper, we propose a secure rational delegation FL scheme, namely SRDFL, and analyze its protection and convergence performance. Specifically, we first utilize the zero-determinant strategy to construct a FL rational model. It delegates tasks to multiple servers and encourages them to perform correct aggregation. Then, we design a bounded DP protection mechanism to achieve a fixed universe of perturbation outputs in a threshold-constrained manner. Finally, based on Shamir’s secret sharing, we propose a trusted verification algorithm of DP to validate servers for correct aggregation. Detailed theoretical analysis and extensive performance evaluations demonstrate that our proposed scheme is effective. Compared to existing works, SRDFL is able to improve 2.72% - 47.92% model accuracy.

Federated learning (FL) enables multiple clients to exchange gradients to facilitate collaborative training in edge computing without transferring private data to the cloud server. However, the malicious server may disrupt the model training process by obtaining gradients to infer client information or faking validation results. Considering these threats, we construct a dynamic, secure, and verifiable FL scheme, named DSVFL. Specifically, we propose the fine-grained top-$k$gradient selection algorithm to accelerate model convergence, which can improve model accuracy by up to 49.14%. Furthermore, we propose a single-cloud secure aggregation protocol that guarantees the server cannot access the real model parameters. We also design a trusted verification algorithm to validate the server's aggregation results while resisting collusion between the server and clients. For federation dynamics, we adopt the Shamir's secret sharing in both the aggregation and verification phases, which enables the correct aggregation and decryption of ciphertexts and the independent verification of client signatures, even in cases of client dropouts. Rigorous theoretical analysis demonstrates that DSVFL can protect data privacy and ensure correct training results. Experimental results indicate that DSVFL can reduce clients' computational cost by 48%-63% and communication overhead by 33%-49% compared with existing solutions.

Future 6 G networks are envisaged to tightly integrate communication, sensing, and computing, demanding real-time, intent-driven intelligence at the edge. Whilelargelanguagemodels (LLMs) excel in intent recognition and semantic reasoning, their application to real-time network lifecycle management at the edge is limited by heterogeneousapplicationintents (APPIs), dynamic network conditions, and severe resource constraints. This paper proposes a novel lightweight LLM architecture, KGLlama-KD, that synergizes knowledge graphs (KGs) withknowledgedistillation (KD) to enable intent-driven networking and enhance 6 G edge intelligence. Specifically, a KG is constructed to formally describe the relationships among application scenarios, functional primitives, performance requirements within APPIs, and the correspondences between APPIs andnetworkservicerequests (NSRs), thereby producing a structured intent training dataset. Building upon the Llama 3 foundation model, a two-phase optimization framework is designed to support lightweight edge deployment while preserving translation fidelity. The LLM is first fine-tuned with KG guidance and compressed via KD in the cloud, and then deployed on resource-constrained edge nodes to perform real-time, accurate, and efficient APPIs interpretation. Experiments validate that KGLlama-KD achieves 95% accuracy for APPI understanding, surpassing DeepSeek and Qwen by an average of 8%. The distilled model reduces inference latency by 60% compared to full-scale LLMs, fulfilling the sub-100 ms requirement for 6 G latency-sensitive services.

Federated Learning (FL) enables collaborative model training across multiple parties while preserving data privacy. It has been widely adopted in sensitive domains such as healthcare and finance. As a result, the trained models themselves have become critical intellectual property. To prevent unauthorized use or distribution, watermarking techniques are commonly employed to verify model ownership. However, these mechanisms introduce new attack surfaces. Existing attacks typically assume the presence of watermarks and focus on their removal or forgery, overlooking a critical preliminary step: detecting whether a watermark exists. Furthermore, current approaches often rely on coarse-grained perturbation strategies that degrade model performance, limiting the practicality of such attacks. This paper proposes FedProbe, an attack framework from the perspective of an internal FL participant. FedProbe aims to detect and selectively remove embedded watermarks while minimizing the impact on model utility. It first detects the presence of a watermark by combining minimal inversion perturbation optimization with outlier detection. Then, it identifies critical pathways by analyzing neuron-level response differences under reversed triggers and performs fine-grained interventions via targeted neuron-level parameter replacement. Experimental results show that FedProbe achieves over 99% confidence in watermark detection across three mainstream datasets and various watermarking methods. Simultaneously, it reduces watermark verification success rates to 24% while keeping primary task performance degradation within 3%, demonstrating strong generalizability and robustness.

The burgeoning interest in cloud computing and trusted execution environments (TEEs) has prompted a focus on enhancing the performance of diverse queries on encrypted geo-textual data within public cloud infrastructures. Secure spatial keyword range queries are pivotal for applications that rely on cloud assistance. However, current approaches to SRSK queries are inadequate for multi-user settings or demand excessive client-side resources to process queries.In this paper, we present RASKEE, a novel SRSK query model integrating conventional cryptographic techniques with TEEs. RASKEE is designed to support a multi-user environment, offloading most computational and storage demands from clients to the public cloud. Theoretically, RASKEE minimizes client-side storage and computational overheads and provably achieves IND-CKA2 security. Empirical evaluations on three real-world datasets demonstrate that RASKEE outperforms state-of-the-art solutions by up to 0.5 − 4 orders of magnitude in terms of response time for clients.

Face recognition systems are vulnerable to adversarial attacks, where imperceptible perturbations can mislead model predictions. However, existing methods often lack fine-grained control over perturbation localization and fail to effectively exploit structural and semantic priors. In this paper, we propose a multimodal adversarial framework termed structure-aware entropy masked attack (SEMA) that integrates structural entropy-guided masking and semantic alignment. By leveraging multimodal information and diffusion-based generation, the method injects perturbations into high-entropy regions to maximize structural disruption while maintaining perceptual fidelity. Furthermore, we conduct an information-theoretic analysis from both topological and embedding perspectives to interpret the adversarial generation process. Experiments indicate SEMA outperforms previous methods on diverse face recognition models, with the attack success rate improved by up to 18.6% in the best case.

With the growing reliance on cloud services for large-scale data management, preserving the security and privacy of outsourced datasets has become increasingly critical. While encrypting data and queries can prevent direct content exposure, recent research reveals that adversaries can still infer sensitive information via access pattern and search path analysis. However, existing solutions that offer strong access pattern privacy often incur substantial performance overhead. In this paper, we propose a novel privacy-preserving range query scheme over encrypted datasets, offering strong security guarantees while maintaining high efficiency. To achieve this, we develop secure l earned spatial index (SLS-INDEX), a secure learned index that integrates the Paillier cryptosystem with a hierarchical prediction architecture and noise-injected buckets, enabling data-aware query acceleration in the encrypted domain. To further obfuscate query execution paths, SLS-INDEX- based Range Queries (SLRQ) employs a permutation-based secure bucket prediction protocol. Additionally, we introduce a secure point extraction protocol that generates candidate results to reduce the overhead of secure computation. We provide formal security analysis under realistic leakage functions and implement a prototype to evaluate its practical performance. Extensive experiments on both real-world and synthetic datasets demonstrate that SLRQ significantly outperforms existing solutions in query efficiency while ensuring dataset, query, result, and access pattern privacy.

Black-box adversarial attacks require only querying model outputs rather than accessing internal gradients, making them more covert and posing a substantial threat to the security of deep neural networks. However, existing score-based black box methods typically rely on single-scale finite difference gradient estimation; the step size is constrained by a pronounced tradeoff between bias and variance, which in high-dimensional settings often inflates estimation variance and destabilizes the estimated direction. To address this, we propose Robust Multi-Scale Gradient Estimation (RMSGE) to improve query efficiency and stability for black box attacks. RMSGE uses a multi-scale finite difference strategy to mitigate the conflict between bias and variance and integrates two heterogeneous sampling schemes, Natural Evolution Strategy (NES) and Simultaneous Perturbation Stochastic Approximation (SPSA), to enhance both global exploration and local sensitivity in gradient estimation. In addition, RMSGE applies momentum smoothing across scales and sampling distributions to suppress noise accumulation in high-dimensional spaces and employs a voting mechanism to fuse gradient information from multiple sources, further improving the stability and robustness of the update direction. Theoretical analysis shows that RMSGE preserves the first-order unbiasedness of finite difference estimation while effectively alleviating the tradeoff between bias and variance. Extensive experiments demonstrate that RMSGE achieves substantially higher attack success rates and query efficiency than existing black box methods across multiple benchmark datasets, validating its effectiveness.

Replication-based outsourced computation enables efficient correctness verification through cross-checking results from multiple non-colluding clouds, yet introduces critical privacy risks when clouds exchange intermediate data for interest alignment, especially in multi-user private data scenarios such as federated learning. To address this challenge, we propose BDT-RVOC, a novel framework integrating binary-segmentation data truncation with distributed multi-trapdoor public key cryptography (DMT-PKC). BDT-RVOC dynamically splits raw data into mutually exclusive blocks distributed to different clouds, ensuring that computations on truncated data preserve bit-level consistency with raw-data operations while preventing raw-data leakage during inter-cloud exchanges. Our framework designs secure interactive protocols for addition, multiplication, and equivalence testing under truncation, formally guaranteeing operational equivalence between block-level and raw-data computations. By extending Paillier encryption to a multi-key setting, DMT-PKC partitions strong private keys to resist collusion between the aggregator and up to n−2 agents. Applied to private aggregation, BDT-RVOC achieves a 1.8× speedup over federated learning baselines with 57% lower communication overhead. Theoretical analysis proves resilience against semi-honest adversaries compromising all communications and up to n−1 colluding parties, while experiments on real-world datasets confirm practical efficiency and scalability for real-world deployments.

Abstract Adversarial attacks on speaker identification (SI) systems have become a critical security concern, particularly in targeted black-box scenarios where access to the target model is limited. This paper proposes a novel framework that creates highly transferable adversarial examples. We use a voice conversion (VC) model to synthesize shadow data from a single target speech sample, which is then used to train two diverse surrogate models. Neural Tangent Kernel (NTK) theory is employed to align acoustic feature spaces, while mutual information optimization enforces consistency between the surrogate models’ predictions. Consequently, the adversarial attack is formulated as a min-max game that maximizes attack success while preserving speech quality. Extensive experiments on LibriSpeech and VCTK datasets demonstrate that our method significantly improves the transferability and effectiveness of adversarial examples compared to conventional approaches. Our findings suggest that generating shadow data through voice conversion followed by surrogate model training under information-theoretic constraints is a promising strategy for robust adversarial attacks.

The Industrial Internet of Things (IIoT) achieves the automation, monitoring, and optimization of industrial processes by interconnecting various sensors, smart devices, and the Internet, which dramatically increases productivity and product quality. Nevertheless, the IIoT comprises a substantial amount of sensitive data, which requires encryption to ensure data privacy and security. Recently, Sun et al. proposed a certificateless searchable encryption scheme for IIoT to enable the retrieval of ciphertext data while protecting data privacy. However, we found that their scheme not only fails to satisfy trapdoor indistinguishability but also lacks defense against keyword guessing attacks. In addition, some schemes use deterministic algorithms in the encryption process, resulting in the same ciphertexts after encryption for the same keyword, thereby leaking the potential frequency distribution of the keyword in the ciphertext space, thereby leaking the potential frequency distribution of the keyword in the ciphertext space, allowing attackers to infer the plaintext information corresponding to the ciphertext through statistical analysis. To better protect data privacy, we propose an improved certificateless searchable encryption scheme with a designated server. With security analysis, we prove that our scheme provides multi-ciphertext indistinguishability and multi-trapdoor indistinguishability security under the random oracle. Experimental results show that the proposed scheme has good overall performance in terms of computational overhead, communication overhead, and security features.

In edge computing environments, the rapid growth of Internet of Things (IoT) devices presents significant challenges for data processing. These devices are often resource-constrained, leading to a tradeoff between achieving efficiency and ensuring security. On one hand, traditional certificateless encryption methods are computationally expensive; on the other hand, offloading the computational load to third-party entities can enhance efficiency but still introduces security risks. To address these issues, this article proposes a novel lightweight certificateless edge-assisted encryption scheme (CL-EAED). Ours scheme offloads computationally intensive tasks to edge servers, ensuring that edge-assisted processing does not expose sensitive information and only needs to be performed once. This approach effectively prevents data leakage and enhances both the efficiency and security of task offloading. Moreover, the CL-EAED scheme achieves IND-CCA security in standard model (SM) and has been validated using the ProVerif tool. Experimental evaluations demonstrate that CL-EAED eliminates the dependency on computationally intensive pairing operations, significantly reducing computational and communication costs. It outperforms existing solutions in terms of energy consumption, latency, and scalability, fully meeting the requirements of practical applications.

The rapid growth of Internet of Things (IoT) technology has heightened the requirement for effective data management and analysis. Knowledge graphs (KGs) and large pretrained language models (LLMs) play crucial roles in this scenario: KGs offer structured data management, while LLMs enhance data feature analysis. However, as data privacy concerns escalate, IoT machine learning models become more susceptible to membership inference attacks (MIAs). To tackle this challenge, we focus MIAs in knowledge reasoning models (KRMs) for IoT environments and propose two attack methods: 1) correlation attack (CA) and 2) feature aggregation attack (FAA). CA leverages the relational features of KGs to link member characteristics across different parameter spaces. It aggregates these features and maps them into a nonlinear space to identify linear relationships among members, thus improving membership recognition. In contrast, the FAA focuses on aggregating multiple member features, such as confidence scores, loss values, decision labels, and so on, within the KRM and projects them into a linear space. This method captures the interactions among different features, enhancing the differentiation between member and nonmember samples. The key difference is that CA explores correlations between features across member identities, while FAA aggregates various features to improve overall representation and identification. Experimental results show that both CA and FAA outperform existing methods, offering a more effective assessment of privacy risks in KRMs within IoT environments.

In mobile crowdsensing (MCS) applications, the single type data is inadequate to reflect the complexities of the real world and meet precise task requirements. Currently, there are few works that focus on mixed data in the context of MCS, and there is no work considering the credit issues of sensing platform. The privacy, fairness, and reliability of assessing the quality of mixed data remain unguaranteed. Therefore, we design a high-efficiency and privacy-preserving mixed data quality assessment scheme which adopts a dual-server architecture, designs secure k-prototype clustering for quality assessment, and conducts anomaly detection to eliminate anomalous data. Furthermore, we design a fair and reliable allocation mechanism to fairly allocate reward to users based on fixed and floating reward mechanisms for incentivizing rational users to submit high-quality mixed data. To prevent payment defaults by the sensing platform, we design verifiable credential to restrict them, ensuring payment fairness and transactional reliability. Finally, through theoretical analysis and experimental evaluation, we demonstrate the effectiveness and security of the proposed scheme. The results indicate that in terms of efficiency, the time overhead of mixed data quality assessment has been significantly reduced by three orders of magnitude compared to existing schemes.

The intelligent vehicle (IV) manufacturing industry is flourishing, but its large-scale adoption and application require not only ensuring driving safety and high-quality service but also addressing a multitude of data security threats. Among these, authentication and unauthorized access are significant factors hindering its implementation. To ensure the secure communication and operation of IVs, we propose an new matchmaking encryption scheme with inner product, which supports bilateral control, as well as authentication with anonymity. As a cornerstone of this article, we design a secure attribute-based anonymous credential scheme, enabling authentication. Moreover, by introducing the dual-policy of inner product, the authenticating process ensures the vehicle privacy through anonymity. We formalize the definitions of authentication and anonymity and prove that our scheme satisfies these properties. Compared with the most related works, the experimental results demonstrate that our scheme provides enhanced security while maintaining comparable computational overhead. Notably, the computational performance in encryption, verification, and decryption processes is impressive. The high efficiency of these processes makes them well-suited for resource-constrained devices in IVs.

Delegation Learning(DL) flourishes data sharing, enabling agents to delegate data to the cloud for model training. To preserve privacy, homomorphic encryption (HE) offers an effective solution for privacy-preserving machine learning (PPML) in delegation learning, yet faces critical challenges in functionality (non-linear activation support), practicality (ciphertext blow-up from iterative computations), and security (data leakage risks caused by public knowledge of the mathematical principles applied in model training). To tackle these challenges, we propose THC-DL, a three-party HE framework addressing these challenges holistically for the first time. We elaborately design the ciphertext secure comparison (DL-CSC) protocol to satisfy secure comparison with private inputs, enabling efficient non-linear operations with O(1) communication complexity that reduces runtime to 12.5% of the DGK (Dolev-Greensmith-Kent protocol, the well-known comparison protocol). Second, we construct a Truncation-Mapping (Tru-Map) scheme, a mechanism that transforms input data by truncating and mapping it into a domain that facilitates more efficient processing, and the addition of truncated mapped data to resolve ciphertext blow-up by adaptively scaling ciphertexts during iterative training, ensuring correctness. Third, we formalize data leakage risks in HE-based quadratic convex optimization (standard in ML) and apply THC-DL to construct a secure optimization scheme. Theoretical analysis confirms THC-DL’s resilience against input recovery attacks, even when adversaries exploit public model parameters. Experiments on a real-world platform validate DL-CSC’s efficiency and scalability while reducing the computational complexity and communication complexity from O(n) to O(1) where n denotes the length of the input bits.

Metaverse allows users to immersively interact with millions of partners, breaking through the constraints of the physical world. To ensure the traceability of malicious avatars, provers in large-scale commercial activities need to periodically submit identity parameters to the verifier as interaction evidence, imposing a disastrous communication and storage burden on both parties. In this article, we propose a traceable and efficient continuous authentication scheme for large-scale avatars. First, we construct a continuous authentication framework, where the prover’s device locally checks its manipulator to submit the starting and ending identities as interaction evidence. Second, we propose a transitive signature scheme with path verifiability to ensure the relevance and unforgeability of the starting and ending identities. Finally, we design a traceable authentication protocol based on the proposed signature scheme to reduce communication and storage costs while guaranteeing traceability. Security analysis shows that the protocol not only defends against the attacks of device hijacking and false accusations but also supports virtual-to-physical tracking. Extensive evaluations show that the communication cost and storage costs are reduced by 95.97% and 98.35%, respectively, which can be used for avatar authentication and tracking in large-scale commercial activities.

Wireless body area network (WBAN) integrates body sensors to collect and upload various health indicators, which facilitates timely remote healthcare. To realize the real-time monitoring of the patient’s health status while ensuring the security of sensitive data, signcryption schemes with equality test provide a feasible solution. Nevertheless, most of the existing schemes suffer from heavy computational burdens and are vulnerable to some inherent threats, such as offline message recovery attack and known session temporary key attack. Additionally, some of them cannot provide the essential security properties. To cope with these problems, this paper constructs a secure and lightweight signcryption scheme with group equality test, which is suitable for the heterogeneous environment. To formally define the resistance to offline message recovery attack and known session temporary key attack, we first propose two corresponding security models. Then, we introduce a group mechanism to the equality test to support the aforementioned security properties. Besides, we adopt the online/offline construction to avert the time-consuming operations in the real-time data processing phase. Furthermore, our scheme supports message aggregation to reduce the computational overhead of handling multiple messages. Formal security proof shows that our scheme satisfies confidentiality and unforgeability. Compared to several recent schemes, the experimental results demonstrate that our scheme enjoys the best performance in terms of both computational overhead and communication cost. With implementation, our scheme is proved to be feasible for lightweight devices in real-world applications.

In a wireless body area network (WBAN), group pairing among multiple wearable devices enables efficient and secure broadcasting group messages. Existing pairing methods that rely on trusted concentrators, active participation of users, or homogeneous environments are vulnerable to single point of failure and have restricted practicality. In this article, we propose Electrocardiogram-to-Pair (E2P), an electrocardiogram (ECG)-based secure group pairing protocol for WBAN devices that allows the establishment of a shared group key among multiple devices without requiring the user involvement or the central device. First, all wearable devices worn by the same user simultaneously collect ECG signals, which are then pre-processed through filtering and alignment to minimize the effect of noise on the pairing. Next, an adaptive quantization method is designed to reliably quantize the pre-processed ECG signals to generate initial keys. This method is ingenious as it relies on feature transformation and dynamic thresholds generation in the quantization process, which effectively ensures the security of biometrics and increases the key generation rate. Then, the group key is established among devices with initial keys through an improved Cascade method. The simulation results demonstrate that E2P achieves an effective balance the reliability and efficiency in group pairing, with outstanding performance in both security and key generation rate.

Federated Learning (FL) enables collaborative model training across untrusted devices while preserving data privacy. However, malicious participants can still launch attacks, especially poisoning attacks, during model aggregation. To defend against poisoning attacks, we propose a Coulomb Force-based Federated Learning (CFFL), a physics-inspired defense framework that integrates neighborhood Coulomb force into FL security mechanisms. CFFL effectively addresses detection failures in poisoning attacks arising from the “distance concentration” among high-dimensional data. Specifically, firstly, we establishes a local update model for clients, where the similarity between model updates is quantified through local Coulomb resultant force (LCRF), effectively distinguishing malicious and benign updates; Secondly, we develop ak-nearest neighbor (KNN)-based Coulomb resultant force anomaly detection (NCFAD) model that identifies malicious updates by isolating top-koutliers with largest neighborhood factor; Finally, experiment results validate that CFFL outperforms state-of-the-art (SOTA) baselines in defense performance and achieves model accuracy equivalent to the baseline FedAvg on NCI-1, PROTEINS_full, and MNIST datasets (90.82%, 92.23%, and 96.49%, respectively). This indicates that CFFL effectively mitigates the impact of poisoning attacks without compromising the benign performance of the aggregation model.

The Internet of Vehicles (IoV), as a core component of intelligent transportation systems, significantly enhances the intelligence level of traffic management by enabling efficient vehicle-to-vehicle (V2V) and vehicle-to-infrastructure information sharing. However, the highly dynamic and open nature of the IoV poses severe security challenges in cross-domain scenarios, mainly due to the lack of trust relationships between different domains, making it difficult to achieve efficient and secure cross-domain authentication(CDA). Existing CDA mechanisms in the IoT context often suffer from high computational complexity, excessive communication overhead, and poor scalability for large-scale deployments. This paper proposes a Batch CDA Protocol based on Dynamic Group Signatures (BCDAP-DGS) to address these issues. The proposed protocol incorporates non-interactive zero-knowledge (NIZK) proofs to achieve secure identity verification without requiring additional data exchange. By leveraging dynamic group signature techniques, BCDAP-DGS supports real-time updates of vehicle membership status and provides conditional anonymity. In addition, an online/offline authentication framework is designed by incorporating vehicle location information to precompute related parameters, thereby significantly improving CDA efficiency. A formal security analysis is conducted under the random oracle model, demonstrating that the proposed protocol satisfies essential security properties, including anonymity, non-frameability, unforgeability, and traceability. Experimental results and performance comparisons show that the proposed protocol outperforms existing schemes in terms of both security and efficiency, making it well-suited for large-scale and highly dynamic IoV CDA scenarios.

Existing membership inference attacks (MIAs) based on adversarial attacks typically introduce excessively large adversarial perturbations to change the model predictions. However, such methods are not only likely to obscure the characteristic memory of the model regarding member data but also blur the subtle characteristic differences between member and non-member data. To address this issue, we propose a novel dynamic adversarial perturbation reduction MIA (DAPR-MIA), which aims to enhance the ability to identify the membership of samples by reducing the perturbation strength. Specifically, DAPR-MIA first conducts a fine-grained analysis of the gradient components of conventional adversarial examples, assessing the impact of each element on the model decision to generate a sensitivity mask. This mask identifies dimensions within adversarial examples that significantly influence model predictions, referred to as sensitive dimensions. Then, by dynamically reducing the perturbation size in these sensitive dimensions, DAPR-MIA gradually approaches the minimum perturbation required to change the prediction result of samples, maximizing the retention of membership features within the sample. Finally, the deviation of the prediction results between the original sample and the adversarial example after perturbation attenuation on the target model is measured to infer whether the sample belongs to a member data. Experimental results show that DAPR-MIA significantly outperforms existing methods on multiple datasets and deep models, demonstrating state-of-the-art attack accuracy.

Metaverse allows users to delegate their AI models to an AI engine, which builds corresponding AI-driven avatars to provide immersive experience for other users. Since current authentication methods mainly focus on human-driven avatars and ignore the traceability of AI-driven avatars, attackers may delegate the AI models of a target user to an AI proxy program to perform impersonation attacks without worrying about being detected.In this paper, we propose an authentication method using multi-factors to guarantee the traceability of AI-driven avatars. Firstly, we construct a user’s identity model combining the manipulator’s iris feature and the AI proxy’s public key to ensure that an AI-driven avatar is associated with its original manipulator. Secondly, we propose a chameleon proxy signature scheme that supports the original manipulator to delegate his/her signing ability to an AI proxy. Finally, we design three authentication protocols for avatars based on the identity model and the chameleon proxy signature to guarantee the virtual-to-physical traceability including both the human-driven and AI-driven avatars.Security analysis shows that the proposed signature scheme is unforgeability and the authentication method is able to defend against false accusation. Extensive evaluations show that the designed authentication protocols complete user login, avatar delegation, mutual authentication, and avatar tracing in about 1s, meeting the actual application needs and helping to mitigate impersonation attacks by AI-driven avatars.

The integration of blockchain-based federated learning (BFL) and Industry 4.0 utilizes intermediate models to execute task deployment and result acceptance, effectively solving the problems of data barriers and data resource waste in Industry 4.0. However, the BFL ecosystem is susceptible to poisoning and inference attacks that undermine data privacy and model security. In this paper, we propose PSFL, a robust FL framework that guarantees both data privacy and model security. Specifically, we design a cross-validation algorithm where numerous participants conduct a thorough assessment of the user’s contribution growth rate in the current round. This approach proves effective in identifying Byzantine attackers engaged in malicious activities within the system. Furthermore, we propose a lightweight multi-receiver signcryption mechanism employing secure key distribution, which significantly minimizes resource overhead. Finally, the security of PSFL is proved based on the random oracle model. Empirical assessment affirms the effectiveness and practicality of PSFL, even with different proportions of malicious users, PSFL’s performance is 10%20% higher than Trimmed Mean and M-Krum. In summary, PSFL improves the model accuracy and the security of the model transmission process in scenarios involving edge node poisoning, which demonstrates that PSFL can be well adapted to Industry 4.0.

Federated Learning (FL) allows clients to keep local datasets and train collaboratively by uploading model gradients, which achieves the goal of learning from fragmented sensitive data. Although FL prevents clients’ datasets from being shared directly, local private information may be leaked through gradients. To mitigate this problem, we combine game theory to design an FL scheme (IMFL) based on the incentive mechanism and differential privacy (DP). Firstly, we explore three DP variants, all of which are resistant to deep leakage from gradients (DLG) but differ in their level of privacy protection. In addition, we perform the convergence analysis of the FL model based on DP. Then, with the assistance of game theory, we analyze the natural state of the server and clients in the FL process and formulate the utility function of both sides under the case of considering the attack. Finally, we establish the optimization problem as a Stackelberg game and solve for the optimal strategy of the server and clients by deriving the Nash equilibrium to achieve personalized protection. Theoretical proof demonstrates that both types of entities can achieve optimal actions by maximizing their utility functions upon reaching the Nash equilibrium. Besides, extensive experiments are conducted on real-world datasets to demonstrate that the IMFL is efficient and feasible.

Mobile crowdsourcing (MCS) is a promising new paradigm for intelligent data perception in large-scale sensor applications such as the Industrial Internet of Things (IIoT). This approach assigns industrial perception tasks to mobile devices for data collection and sharing, creating a bright outlook for building strong industrial systems and improving industrial services. However, the particular IIoT network environment is vulnerable to a range of malicious attacks, including the manipulation or deletion of data. Moreover, industry-aware nodes, which have limited energy resources, are susceptible to various failures that can result in distorted data and inaccurate trend analysis. To tackle the above issues, we propose a dynamic sub-cluster privacy-preserving aggregation (DScPA) scheme for the crowdsourced industrial virtual areas, by exploring the equilibrium between privacy security and data benefits for industrial users. Specifically, we propose joining and exiting the virtual area aggregation system protocol, and design low-cost privacy-preserving aggregation algorithm to achieve flexible and dynamic construction of virtual areas. Additionally, we propose a supervised mechanism-based subset aggregation protocol that takes into account the remaining energy of the nodes in the virtual areas aggregation system and their distance from the base station. We employ the relevant characteristics of polynomial functions and binary data to achieve data privacy protection and integrity verification at a lower computational cost. Furthermore, we develop an asymmetric information iterative static non-cooperative game model to verify the soundness of DScPA. Finally, security analysis demonstrates that the DScPA scheme meets the security objectives. Simulations show that implementing DScPA in the industrial virtual area can improve the network lifetime by about 16.7% compared to existing solutions, while also increasing the average remaining energy of industry-aware nodes.

Using federated learning (FL) to train global models in IoT improves computational efficiency and protects users’ data privacy. However, FL still faces privacy threats. Driven by interests, servers reduce their computational cost or induce wrong decisions in IoT devices by returning wrong global model gradients. Although the verifiability of aggregation results is achieved in previous research, it is difficult to defend against collusion attacks launched by servers and users. Therefore, we construct a rational verifiable federated learning secure aggregation protocol based on the dual-server framework and game theory, which achieves verifiable aggregation results, and effectively defends against collusion attacks. Firstly, we propose a new model verification code based on the property of irreversible matrices, which allows users to verify the correctness of the aggregation results by matrix products. This model validation code is computationally efficient and resistant to the adversary’s backward inference. Secondly, we adopt a dual-server architecture and improve the prisoner contract and betrayal contract according to the actual application scenarios of IoT, converting the previous collusion attacks between servers and users to collusion attacks between servers and making the rational servers not launch collusion attacks to destroy the verification mechanism of the aggregation results through the incentive mechanism. Finally, we demonstrate through security analysis that RVFL is secure and effective against collusion and reverse inference attacks. In addition, we show through experimental results that RVFL can improve its efficiency by three orders of magnitude in the verification phase and 88% in the masking phase.

Asynchronous distributed key generation (ADKG) is a strong-robustness key management technology to bootstrap threshold cryptosystems without a global clock, which can enable decentralized security management for threshold digital wallets in blockchain network. However, the high-computational cost of existing ADKG protocols makes it difficult to remove the “slow” connotation from the word “asynchronous” in a high-threshold security context. In this article, we propose a simpler “two-high” ADKG protocol (H2CT) for blockchain to improve the computational efficiency of asynchronous communications while balancing it with threshold security. Concretely, we first construct a computationally efficient asynchronous complete secret sharing (ACSS) scheme using number theory transformation, reducing the computational complexity of share evaluation from$O(n^{2})$to$O(n\log n)$in the dealing phase. To eliminate the negative impact of up to t biased secrets brought by the implicate messages in the agreement phase, we extend the verifiable ACSS scheme to a publicly verifiable ACSS scheme (pvACSS) using Feldman polynomial commitment. Leveraging this enhanced randomness, the matrix computation cost and message size in the remaining phases are reduced to about half that of the existing works, i.e.,$O(2n^{2})$. Finally, considering the high-scalability requirements, the network size n is increased to up to 1024 nodes and the results show that our pvACSS and the derived H2CT reduce the runtime by approximately 33.96 s and 824.46 s, respectively, over the state-of-the-art. Moreover, we perform simulations on an open-source blockchain testbed, fully demonstrating the efficiency advantages of our H2CT protocol.

In recent years, the cloud infrastructure has been developed as a promising sharing system for the Internet of Vehicles (IoV) communication. During the information exchange, search service over ciphertext called searchable encryption (SE) is an extraordinary method to prevent data breaches. However, two open problems still need to be solved for ranked search, which hinders the application practicality in IoV. First, each data owner must store the extra information to distribute weight values dynamically. Second, ranking in the cloud has not been supported by most existing schemes. In this article, to address the above problems and fit the characteristics of real-time data exchange in IoV, we present a multikeyword-ranked search scheme supporting extreme environments for the IoV. Specifically, our system designs a unique encrypted index tree structure to realize the multikeyword-ranked retrieval, the weight value dynamic adaptive calculation, and dynamic updating in IoV. Moreover, we use a primary–secondary dual-server model to cope with extreme environments and propose a “greedy breadth-first search” algorithm to achieve an effective sublinear search. Finally, comprehensive security analysis and experimental simulation for the proposed system prove that our system can guarantee user privacy and acceptable efficiency.

Delegation learning is indeed a prevalent approach in privacy-preserving machine learning (PPML), especially when dealing with big data. It specifically involves data owners delegating their data to servers with computational capabilities for training and inference. These servers provide services on a pay-per-use basis. The essence of delegation learning lies in maintaining the integrity of the server’s training while ensuring the privacy of the delegator’s data. However, existing delegation learning schemes struggle to balance security and efficiency, and they cannot guarantee correctness. To tackle these challenges, we propose a cloud-edge collaborative delegation learning framework (CEC-DL) against covert adversaries, which is verifiable and satisfies the guaranteed output delivery (GOD) in security. This is the first time that the covert security assumption is used in a PPML scenario. Furthermore, we design probabilistic verifiable secure addition and subtraction computation protocol (PVS-AaS) and probabilistic verifiable secure multiplication computation protocol (PVS-MUL), which can be used to realize secure addition and multiplication computations in delegation learning without expensive message authentication code (MAC) verification. At the same time, we develop a malicious adversary detection protocol (MADP) that could prevent the malicious actions of potential covert adversaries while ensuring the correct output. Finally, we apply the CEC-DL to the linear regression model to construct privacy preserving linear regression protocol (PP-LRP). Through theoretical analysis and experiments, CEC-DL improves the security, and is more efficient than the verifiable computation of malicious adversaries.

With the goal of defending against false data injection attacks (FDIAs) on state estimation (SE) of the Internet of Things (IoT)-based smart grid, recently, the reactance perturbation strategy (RPS) has been proposed by actively perturbing the branch reactances of transmission lines. Satisfied defending performance as it shows, the limitations have not been sufficiently studied by pioneer works. In this article, by exploring the vulnerability implied by the transmission network structure, we deeply investigate the limitations of RPS with both theoretical and numerical results. First, we prove that improperly selecting the branches to perturb can make RPS fail to prevent the SE from FDIAs. Second, by exploiting the properties of the system topology, we classify the branches and buses into different types and derive the limitations of RPS with analytical results. Third, an enhanced RPS is proposed to defend against FDIAs with a complete defense goal. Finally, extensive simulations are conducted in IEEE test power systems to verify the correctness of analytical results and validate the effectiveness of the enhanced RPS.

Federated learning of deep neural networks has emerged as an evolving paradigm for distributed machine learning, gaining widespread attention due to its ability to update parameters without collecting raw data from users, especially in digital healthcare applications. However, the traditional centralized architecture of federated learning suffers from several problems (e.g., single point of failure, communication bottlenecks, etc.), especially malicious servers inferring gradients and causing gradient leakage. To tackle the above issues, we propose a robust and privacy-preserving decentralized deep federated learning (RPDFL) training scheme. Specifically, we design a novel ring FL structure and a Ring-Allreduce-based data sharing scheme to improve the communication efficiency in RPDFL training. Furthermore, we improve the process of distributing parameters of the Chinese residual theorem to update the execution process of the threshold secret sharing, supporting healthcare edge to drop out during the training process without causing data leakage, and ensuring the robustness of the RPDFL training under the Ring-Allreduce-based data sharing scheme. Security analysis indicates that RPDFL is provable secure. Experiment results show that RPDFL is significantly superior to standard FL methods in terms of model accuracy and convergence, and is suitable for digital healthcare applications.

Well-trained neural network models are deployed on edge servers to provide valuable inference services for clients. To protect data privacy, a promising way is to exploit various types of secret sharing to implement privacy-preserving neural network inference. However, existing schemes suffer high communication rounds and overhead, making them hardly practical. In this paper, we propose Cenia, a new communication-efficient privacy-preserving neural network inference model. Specifically, we exploit arithmetic secret sharing to develop low-interaction secure comparison protocols, that can be used to realize secure activation layers (e.g., ReLU) and secure pooling layers (e.g., max pooling) without expensive garbled circuit and oblivious transfer primitives. Besides, we also design secure exponent and division protocols to realize secure normalization layers (e.g., Sigmoid). Theoretical analysis demonstrates the security and low complexity of Cenia. Extensive experiments have also been conducted on benchmark datasets and classical models, and experimental results show that Cenia achieves privacy-preserving, accurate, and efficient neural network inference. Particularly, Cenia can achieve 37.5% and 60.76% of Sonic’s communication rounds and overhead, respectively, compared to Sonic (i.e., the state-of-the-art scheme).

Face and voice are two of the most popular traits used for authentication tasks in daily life, as they can be easily captured using low-cost visual and audio sensors on smartphones, laptops, tablets,etc. Many bimodal biometric authentication schemes based on these two traits have been presented to provide higher accuracy than unimodal systems. However, these schemes are inflexibility due to the requirement of submitting two traits simultaneously, and they lack template protection, which may lead to biometric data leakage. We present a cross-modal learning based bimodal biometric authentication scheme, which improves the flexibility of existing schemes while ensuring the biometric template security. We integrate cross-modal learning into the feature extraction to obtain a bimodal biometric shared representation given input face images and voice clips. In order to enhance biometric template security without sacrificing authentication accuracy, a residual network and polar codes based template protection method is proposed, which can eliminate the noise in shared representations due to intra-user variations and generate protected templates. We have evaluated the efficacy of the bimodal biometric scheme using a real video dataset containing face images and voice clips. Experimental results demonstrate that our scheme can achieve flexible authentication with high accuracy no matter the probe input is a face image, a voice clip or a combination of them. Furthermore, the security analysis demonstrates that our scheme provides irreversibility, unlinkability and revocability of protected templates.

Metaverse is a vast virtual world parallel to the physical world, where the user acts as an avatar to enjoy various services that break through the temporal and spatial limitations of the physical world. Metaverse allows users to create arbitrary digital appearances as their own avatars by which an adversary may disguise his/her avatar to fraud others. In this paper, we propose an anti-disguise authentication method that draws on the idea of the first impression from the physical world to recognize an old friend. Specifically, the first meeting scenario in the metaverse is stored and recalled to help the authentication between avatars. To prevent the adversary from replacing and forging the first impression, we construct a chameleon-based signcryption mechanism and design a ciphertext authentication protocol to ensure the public verifiability of encrypted identities. The security analysis shows that the proposed signcryption mechanism meets not only the security requirement but also the public verifiability. Besides, the ciphertext authentication protocol has the capability of defending against the replacing and forging attacks on the first impression. Extensive experiments show that the proposed avatar authentication system is able to achieve anti-disguise authentication at a low storage consumption on the blockchain.

Although differential privacy metaverse data sharing can avoid privacy leakage of sensitive data, randomly perturbing local metaverse data will lead to an imbalance between utility and privacy. Therefore, this work proposed models and algorithms of differential privacy metaverse data sharing using Wasserstein generative adversarial networks (WGAN). Firstly, this study constructed the mathematical model of differential privacy metaverse data sharing by introducing appropriate regularization term related to generated data's discriminant probability into WGAN. Secondly, we established basic model and algorithm for differential privacy metaverse data sharing using WGAN based on the constructed mathematical model, and theoretically analyzed basic algorithm. Thirdly, we established federated model and algorithm for differential privacy metaverse data sharing using WGAN by serialized training based on basic model, and theoretically analyzed federated algorithm. Finally, based on utility and privacy metrics, we conducted a comparative analysis for the basic algorithm of differential privacy metaverse data sharing using WGAN, and experimental results validate theoretical results, which show that algorithms of differential privacy metaverse data sharing using WGAN maintaining equilibrium between privacy and utility.

The swift progression of the Internet of Things and the extensive integration of machine learning have spurred the growth of intelligent healthcare. Many intelligent healthcare devices, limited by their own computing and storage resources, require outsourcing data analysis tasks to cloud platforms for efficient and accurate results. Unfortunately, malicious cloud services lead to privacy breaches in outsourced data and untrustworthiness in learning models. To address these challenges, this paper proposes a verifiable privacy-preserving outsourced prediction scheme based on blockchain in smart healthcare (VPOL). Specifically, by incorporating blockchain technology into VPOL, we build a robust and scalable framework to prevent falsification of outsourced data and learning models in a decentralized and transparent manner. Then, we design a training committee approach to ensure the reliability of outsourced prediction and employ homomorphic encryption and commitment scheme to protect the privacy and integrity of the data. Finally, theoretical analysis proves the effectiveness and security of VPOL. Sufficient experiments demonstrate that VPOL achieves the approximate accuracy of the plaintext.

The emergence of Vertical Federated Learning (VFL) addresses the issue of data isolation and enhances edge intelligence. However, the high computational costs pose a significant challenge when employing Homomorphic Encryption (HE) for privacy protection in VFL. To address this challenge, we present DFedXGB, a secure and reliable framework for Federated XGBoost (FedXGB). DFedXGB incorporates an innovative data desensitization scheme to safeguard raw data and model parameters against privacy disclosure. Furthermore, We also designed a secure aggregation algorithm based on XGBoost to mitigate server collusion. Theoretical analysis confirms the correctness of DFedXGB, and security analysis establishes its provable security. Experimental results on real datasets demonstrate that DFedXGB achieves lossless accuracy comparable to non-privacy-preserving centralized methods. Moreover, DFedXGB reduces computational costs by an average of 85% compared to SecureBoost.

Abstract As an important approach to overcome data silos and privacy concerns in deep learning, federated learning, which can jointly train the global model and keep data local, has shown remarkable performance in a range of industrial applications. However, federated learning still suffers from the problem that shared gradients may be subject to tampering, inference functions, and falsification. To address this issue, we propose a verifiable federated learning framework to deal with malicious aggregators. Initially, we propose a reputation calculation mechanism to solve the problem of selecting a reliable aggregator based on a multiweight subjective logic model. Furthermore, we design a verifiable federated learning scheme to ensure data confidentiality, integrity, and verifiability, as well as support the client's dynamic withdrawal. Security analyses indicate that our framework is secure against malicious adversaries. Furthermore, experimental results on real datasets show that our verifiable federated learning has high accuracy and feasible efficiency.

Connected autonomous vehicles (CAVs) are capable of capturing high-definition images from onboard sensors, which can be used to facilitate the detection of objects in the vicinity. Such images may, however, contain sensitive information (e.g., human faces and license plates) as well as the indirect location of CAVs. To protect the object privacy of images shared by CAVs, this article proposes a privacy-preserving object detection (P2OD) framework. Specifically, we propose multiple secure computing protocols designed to construct a privacy-preserving Faster$R$-convolutional neural network (CNN) model to securely extract features and bounding-boxes of objects in an image. By leveraging edge computing (with higher performance computation and lower latency, in comparison to cloud-based solutions), CAVs randomly split the captured images and upload them to two noncollusive edge servers. Both servers will then perform the P2OD framework cooperatively to directly detect objects over random image shares without exposing sensitive information. The theoretical analysis demonstrates the security, correctness, and efficiency of the P2OD framework, and the experimental findings show that the P2OD framework can effectively protect the classification and location privacy of image objects for CAVs. Compared with the original Faster R-CNN model, the classification and regression errors of the P2OD framework can be controlled within 10−12 and 10−14, respectively.

Metaverse is a vast virtual environment parallel to the physical world in which users enjoy a variety of services acting as an avatar. To build a secure living habitat, it’s vital to ensure the virtual-physical traceability that tracking a malicious player in the physical world via his avatars in virtual space. In this paper, we propose a two-factor authentication framework based on biometric-based authentication and chameleon signature. First, aiming at disguise in virtual space, we design an avatar’s two-factor identity model to ensure the verifiability of avatar’s virtual identity and physical identity. Second, facing at authentication efficiency and keys holding cost, we propose a chameleon collision signature algorithm to efficiently ensure that the avatar’s virtual identity is associated with its physical identity. Finally, aiming at impersonation in the physical world, we design two decentralized authentication protocols based on the avatar’s identity model and the chameleon collision signature to achieve real-time authentication on the avatar’s identity. Security analysis indicates that the proposed authentication framework guarantees the consistency and traceability of the avatar’s identity. Simulation experiments show that the framework not only completes the decentralized authentication between avatars but also achieves virtual-physical tracking.

As a critical infrastructure, the traditional power system has transformed into a cyber-physical integrated smart grid. However, the vulnerabilities exposed in either the cyber or physical layer might be exploited by adversaries to construct complicated and coordinated attacks consequent in destructive impacts. In this paper, we propose a stealthy physics-manipulated attack (SPMA) by masking the physical attacks on the flexible AC transmission system (FACTS) with strategic cyberattacks. To construct the SPMA, we first manipulate the control command sent to the FACTS device to change the reactance and then tamper with the sensor measurements to conceal it. The SPMA is constructed with complete-informed and incomplete-informed attackers, noisy sensor measurements, and a nonlinear AC model, respectively. The impact of the physics manipulation on the real-time economic dispatch and the system’s operation security are formulated and numerically analyzed. Furthermore, we also provide potential countermeasures from three aspects to defend against SPMAs. Finally, extensive experiments are conducted with the IEEE test power systems to evaluate the stealthiness of SPMAs and the economic losses and potential cascading failures caused by SPMAs using real-world load profiles.

The 5G-enabled Industrial Internet of Things tilts the data processing model from the cloud to the edge. Users are more inclined to get feedback and data analysis of outsourcing computing results timely from the edge. However, the existing solutions undermine the fairness of multitask outsourcing in edge environment and cannot guarantee the correctness of results. To tackle these challenges, in this article, we propose a fair, verifiable, and privacy-preserving edge outsourcing computing scheme based on blockchain (FVP-EOC). Initially, we propose a task bidding method in the same round of task outsourcing, which improves the utilization of resources and the fairness of the FVP-EOC by dividing tasks into blocks. Furthermore, we design a result verification algorithm and a consensus algorithm to ensure the correctness of the results without a trusted third party. Finally, theoretical analysis and ample simulations indicate that the FVP-EOC is secure and verifiable and ensure the benefits of all the participants in edge outsourcing computing.

Federated learning breaks down data silos and promotes the intelligence of the Industrial Internet of Things (IIoT). However, the principal–agent architecture commonly used in federated learning not only increases the cost but also fails to take into account the privacy protection and trustworthiness of flexible on-demand data sharing. To tackle the above challenges, we propose a secure and trusted federated data sharing (STFS) based on blockchain. Initially, we construct an autonomous and reliable federated extreme gradient boosting learning algorithm to crack the data isolation problem, providing privacy protection and verifiability. Furthermore, we design a secure and trusted data sharing and trading mechanism to ensure secure on-demand controlled data sharing and fair trading. Finally, the security of STFS is proved based on the universal composable theory. The results of ample experimental simulations demonstrate the good effectiveness and performance of STFS for IIoT applications.

Aiming to the data and model privacy issue in outsourced clustering tasks, this paper proposes an practical privacy-preserving k-prototype clustering scheme (referred to PriKPM) supporting mixed numerical and categorical attributes data. In PriKPM scheme, the users only randomly split the data sample into two shares and send them to two non-collusive servers, without interacting with the servers online. The two servers can cooperate to perform secure sample distance calculation, cluster center selection, and in-cluster sample update operations over two randomness data shares, and finally obtain the clustering distribution of samples. Specifically, we design an efficient secure comparison protocol based on additive/arithmetic secret sharing, which can switch freely between "greater than or equal" and "equal" functions, providing two comparison forms for PriKPM scheme. Theoretical analysis indicates the security and efficiency of our PriKPM scheme. Experimental results further show that compared to prior work, the clustering time of PriKPM scheme is reduced by 3 orders of magnitude.

Decoding the real structure from the Social Internet-of-Things (SIoT) network with a large-scale noise structure plays a fundamental role in data mining. Protecting private information from leakage in the mining process and obtaining accurate mining results is a significant challenge. To tackle this issue, we present a graph clustering privacy-preserving method based on structure entropy, which combines data mining with the structural information theory. Specially, user private information in SIoT is encrypted by Brakerski–Gentry–Vaikuntanathan (BGV) homomorphism to generate a graph structure in the ciphertext state, the ciphertext graph structure is then divided into different modules by applying a 2-D structural information solution algorithm and a entropy reduction principle node module partition algorithm, and the$K$-dimensional structural information solution algorithm is utilized to further cluster the internal nodes of the partition module. Moreover, normalized structural information and network node partition similarity are introduced to analyze the correctness and similarity degree of clustering results. Finally, security analysis and theoretical analysis indicate that this scheme not only guarantees the correctness of the clustering results but also improves the security of private information in SIoT. Experimental evaluation and analysis shows that the clustering results of this scheme have higher efficiency and reliability.

Collaborative perception enables autonomous vehicles to exchange sensor data among each other to achieve cooperative object classification, which is considered an effective means to improve the perception accuracy of connected autonomous vehicles (CAVs). To protect information privacy in cooperative perception, we propose a lightweight, privacy-preserving cooperative object classification framework that allows CAVs to exchange raw sensor data (e.g., images captured by HD camera), without leaking private information. Leveraging chaotic encryption and additive secret sharing technique, image data are first encrypted into two ciphertexts and processed, in the encrypted format, by two separate edge servers. The use of chaotic mapping can avoid information leakage during data uploading. The encrypted images are then processed by the proposed privacy-preserving convolutional neural network (P-CNN) model embedded in the designed secure computing protocols. Finally, the processed results are combined/decrypted on the receiving vehicles to realize cooperative object classification. We formally prove the correctness and security of the proposed framework and carry out intensive experiments to evaluate its performance. The experimental results indicate that P-CNN offers exactly almost the same object classification results as the original CNN model, while offering great privacy protection of shared data and lightweight execution efficiency.

The smart grid (SG), as one of the largest evolutionary critical infrastructures, witnesses the deep integration of electricity facilities and Internet of Things (IoT). But recent events show that the vulnerabilities exposed in IoT devices can be exploited by adversaries to construct the cyberattacks on SG. To address this threat, plenty of countermeasures have been proposed to enhance the SG’s security. However, the additional costs introduced by some countermeasures make the utilities hesitate to implement them practically. To alleviate this concern, in this article, we propose a double-benefit moving target defense (dB-MTD) to protect the SG from cyber–physical attacks (CPAs) and also gain generation-cost benefits. The dB-MTD enables the prevention of stealthy CPAs on the transmission lines by perturbing the reactances with the distributed flexible AC transmission system (D-FACTS). To reduce the infrastructure cost, we minimize the number of required D-FACTS devices for a specific protection goal. Although it needs investment on D-FACTS, we find that the utility can make profits from the generation costs by appropriately setting the reactance perturbations. Therefore, we formulate an optimization problem to compute the optimal reactance perturbations to maximize the generation-cost benefits, without sacrificing the protection performance of dB-MTD. Finally, using the real-world load profiles, we conduct extensive simulations to evaluate the impact of CPA on the system operation and the benefits obtained by the dB-MTD from the aspects of the D-FACTS deployment and the generation-cost profits.

Edge services provide an effective and superior means of real-time transmissions and rapid processing of information in the Industrial Internet of Things (IIoT). However, the continuous increase of the number of smart devices results in privacy leakage and insufficient model accuracy of edge services. To tackle these challenges, in this article, we propose a blockchain-based machine learning framework for edge services (BML-ES) in IIoT. Specifically, we construct novel smart contracts to encourage multiparty participation of edge services to improve the efficiency of data processing. Moreover, we propose an aggregation strategy to verify and aggregate model parameters to ensure the accuracy of decision tree models. Finally, based on the SM2 public key cryptosystem, we protect data security and prevent data privacy leakage in edge services. Theoretical analysis and simulation experiments indicate that the BML-ES framework is secure, effective, and efficient, and is better suitable to improve the accuracy of edge services in IIoT.

The authentication problem is one of the most significant challenges in the applications of Internet of Things (IoT), and the relationship authentication among smart devices is an effective solution to tackle cross-domain issue. In this article, we first abstract a general undirected graph from the authentication relationship among the smart devices in IoT. Then, we formulate the authentication problem into a signature transitivity problem by incorporating accumulator knowledge and a standard digital signature scheme. As a consequence, the legality of authentication is well-verified by computing the signature and witness of the related edges without worrying about whether the devices belong to the same administrative domain or not. Finally, the efficiency and online time of authority are solved by exploring the blockchain technology, which also leads to the assurance of an online 24-hour third party. The results of analysis and comparison show that the proposed scheme CroDA can well address the practical authentication issue.

Connected autonomous vehicles (CAVs) employ the point cloud data captured by LiDAR to enhance the capability of object recognition and detection. Edge computing with its inherent advantages can help CAVs alleviate resource constraints and enable faster situational awareness and data processing. However, the point cloud data contains private information, such as vehicle identity, location and trajectory, directly uploading the raw point cloud to the edge nodes or other vehicle will lead to serious privacy leakage. To the best of our knowledge, we are the first to try to tackle this challenge and propose a privacy-preserving object detection framework over random point cloud shares for CAVs (referred to SecPCV), aiming to guarantee the privacy of both point cloud and object detection results. In SecPCV, CAVs split point cloud into two random shares based on additive secret sharing (ASS) and upload them to two competing edge nodes, respectively, which greatly compress the computational load of CAVs. Without changing the object detection network in plaintext environment, the edge nodes can cooperatively and securely extract, regress, and classify over point cloud shares. Theoretical analysis ensure the efficiency and security of the SecPCV framework. Experimental results with the real KITTI point cloud dataset indicate that SecPCV can achieve the consistent object detection accuracy as that in plaintext environment, and provide a feasible solution for CAVs secure sharing of point cloud data.

vMixing coins strategy can realize the anonymity of user information, thereby protecting the user's privacy. Ideally, the blacklist is public information and all bad coins are recorded in it. However, due to the failure of some bad coins to be registered in the blacklist in time, users can only obtain part of the blacklist information, which allows illegal criminals to take advantage of it. How to prevent illegal activities under the partial information blacklist and how to design coins' quality updating rule rationally have become open issues in mixing coins. The updating rule of coins' quality in mixing is addressed since illegal criminals may carry out illegal activities, for example, money laundering. ImpSuic, an improved suicide strategy, is proposed as a new quality updating rule. The intuition is: all coins of the one who has the highest bad coins according to the blacklist, are recorded as bad coins. On the other hand, the coins' quality of others remain unchanged. Besides, linear programming is introduced into ImpSuic strategy to predict the maximum utility after mixing coins, which facilitates users to make reasonable decisions before mixing coins. Simulation results show that the quality updating rule in ImpSuic strategy can preserve users' privacy and antimoney launder.

Differential privacy mechanism can maintain privacy-utility monotonicity. Thus, differential privacy mechanism does not obtain privacy-utility balance for numerical data. To this end, we provide privacy-utility balance of differential privacy mechanism with the collaborative perspective in this paper. First, we constructed the collaborative model achieving privacy-utility balance of differential privacy mechanism. Second, we presented the collaborative algorithm of differential privacy mechanism under our collaborative model. Third, our theoretical analysis showed that the collaborative algorithm of differential privacy mechanism could keep privacy-utility balance. Finally, our experimental results demonstrated that the collaborative differential privacy mechanism can maintain privacy-utility balance. Thus, we provide a new collaborative model to solve the privacy-utility balance problem of differential privacy mechanism. Our collaborative algorithm is easy to apply to query processing of numerical data.

The mobile crowdsensing (MCS) technology with a large number of Internet of Things (IoT) devices provides an economic and efficient solution to participation in coordinated large-scale sensing tasks. Edge computing powers MCS to form the mobile edge crowdsensing (MECS) framework. Privacy disclosure of sensing data in multiple stages is a significant challenge in the MECS. To tackle this issue, combining machine learning with game theory, in this article, we propose an artificial intelligence (AI)-enabled three-party game (ATG) framework for guaranteed data privacy in the MECS of IoT. Specifically, based on the random forest classifier and the k-anonymity algorithm, we propose a classification-anonymity model that effectively guarantees the privacy of sensitive data. Moreover, we construct a three-party game model for analyzing the data privacy leakage in different phases in the MECS. Finally, we conduct numerical and theoretical analyses and ample simulations. The results indicate that the ATG framework is effective and efficient, and better suited to the MECS of IoT.

This paper introduces a novel bi-directional con-volutional framework to cope with the large-variance scale problem in scene text detection. Due to the lack of scale normalization in recent CNN-based methods, text instances with large-variance scale are activated inconsistently in feature maps, which makes it hard for CNN-based methods to accurately locate multi-size text instances. Thus, we propose the relationship network (R-Net) that maps multi-scale convolutional features to a scale-invariant space to obtain consistent activation of multi-size text instances. Firstly, we implement an FPN-like backbone with a Spatial Relationship Module (SPM) to extract multi-scale features with powerful spatial semantics. Then, a Scale Relationship Module (SRM) constructed on feature pyramid propagates contextual scale information in sequential features through a bi-directional convolutional operation. SRM supplements the multi-scale information in different feature maps to obtain consistent activation of multi-size text instances. Compared with previous approaches, R-Net effectively handles the large-variance scale problem without complicated post processing and complex hand-crafted hyperparameter setting. Extensive experiments conducted on several benchmarks verify that our R-Net obtains state-of-the-art performance on both accuracy and efficiency. More specifically, R-Net achieves an F-measure of 85.6% at 21.4 frames/s and an F-measure of 81.7% at 11.8 frames/s for ICDAR 2015 and MSRA-TD500 datasets respectively, which is the latest SOTA. The code is available on https://github.com/wangyuxin87/R-Net.

The requirement for data sharing and privacy has brought increasing attention to federated learning. However, the existing aggregation models are too specialized and deal less with users’ withdrawal issue. Moreover, protocols for multiparty entity matching are rarely covered. Thus, there is no systematic framework to perform federated learning tasks. In this paper, we systematically propose a privacy‐preserving federated learning framework (PFLF) where we first construct a general secure aggregation model in federated learning scenarios by combining the Shamir secret sharing with homomorphic cryptography to ensure that the aggregated value can be decrypted correctly only when the number of participants is greater than t. Furthermore, we propose a multiparty entity matching protocol by employing secure multiparty computing to solve the entity alignment problems and a logistic regression algorithm to achieve privacy‐preserving model training and support the withdrawal of users in vertical federated learning (VFL) scenarios. Finally, the security analyses prove that PFLF preserves the data privacy in the honest‐but‐curious model, and the experimental evaluations show PFLF attains consistent accuracy with the original model and demonstrates the practical feasibility.

Blockchain is a “decentralized” system, where the security heavily depends on that of the consensus protocols. For instance, attackers gain illegal revenues by leveraging the vulnerabilities of the consensus protocols. Such attacks consist of selfish mining (SM1), optimal selfish mining ( ϵ-optimal), bribery selfish mining (BSM), and so forth. In existing works, the attacks only consider the circumstances, where part of miners are rational. However, miners are hardly nonrational in the blockchain system since they hope to maximize their revenues. Furthermore, attackers prefer intelligent tools to increase their power for more additional revenues. Therefore, new models are urgently needed to formulate the scenarios, where attackers are purely rational and intelligent. In this paper, we propose a new BSM model, where all miners are rational. Moreover, rational attackers are intelligent such that they optimize their strategies by utilizing reinforcement learning to boost their revenues. More specifically, we propose a new selfish mining algorithm: intelligent bribery selfish mining (IPBSM), where attackers choose optimal strategies resorting to reinforcement learning when they interact with the external environment. The external environment can be further modeled as a Markov decision process to facilitate the construction of reinforcement learning. The simulation results manifest that IPBSM, compared with SM1 and ϵ-optimal, has lower power thresholds and higher revenues. Therefore, IPBSM is a threat no to be neglected to the blockchain system.

Blockchain has been an emerging technology, which comprises lots of fields such as distributed systems and Internet of Things (IoT). As is well known, blockchain is the underlying technology of bitcoin, whose initial motivation is derived from economic incentives. Therefore, lots of components of blockchain (e.g., consensus mechanism) can be constructed toward the view of game theory. In this paper, we highlight the combination of game theory and blockchain, including rational smart contracts, game theoretic attacks, and rational mining strategies. When put differently, the rational parties, who manage to maximize their utilities, involved in blockchain chose their strategies according to the economic incentives. Consequently, we focus on the influence of rational parties with respect to building blocks. More specifically, we investigate the research progress from the aspects of smart contract, rational attacks, and consensus mechanism, respectively. Finally, we present some future directions based on the brief survey with respect to game theory and blockchain.

Dynamic wireless sensor networks (DWSNs) as an important means of industrial data collection are a key part of industrial Internet of Things (IIoT), where security and reliability are important characteristics of trustworthiness. However, due to dynamics, the security of key management is caused by a nontrusted base station (BS) that is easily targeted. For the distribution key management scheme, the avianized BS also causes additional and heavy overhead on sensors. To tackle these issues, in this article, we propose a blockchain-based secure key management scheme (BC-EKM). First, stake blockchain is constructed based on the hybrid sensor network. In addition, we design a secure cluster formation algorithm and a secure node movement algorithm to implement key management, where stake blockchain as a trust machine replaces the majority functions of the BS. Finally, we conduct the security analysis and ample simulations. The results indicate that that the BC-EKM scheme is effective and efficient, and better suited to improve the trustworthiness of DWSNs in the IIoT.

With the rapid digitalization of various industries, mobile crowdsensing (MCS), an intelligent data collection and processing paradigm of the industrial Internet of Things, has provided a promising opportunity to construct powerful industrial systems and provide industrial services. The existing unified privacy strategy for all sensing data results in excessive or insufficient protection and low quality of crowdsensing services (QoCS) in MCS. To tackle this issue, in this article we propose a personalized privacy protection (PERIO) framework based on game theory and data encryption. Initially, we design a personalized privacy measurement algorithm to calculate users' privacy level, which is then combined with game theory to construct a rational uploading strategy. Furthermore, we propose a privacy-preserving data aggregation scheme to ensure data confidentiality, integrity, and real-timeness. Theoretical analysis and ample simulations with real trajectory dataset indicate that the PERIO scheme is effective and makes a reasonable balance between retaining high QoCS and privacy.

Fine-grained representation learning targets to generate discriminative description for fine-grained visual objects. Recently, the bilinear feature interaction has been proved effective in generating powerful high-order representation with spatially invariant information. However, the existing methods apply a fixed feature interaction strategy to all samples, which ignore the image and region heterogeneity in a dataset. To this end, we propose a generalized feature interaction method, named Adaptive Bilinear Pooling (ABP), which can adaptively infer a suitable pooling strategy for a given sample based on image content. Specifically, ABP consists of two learning strategies: p-order learning (P-net) and spatial attention learning (S-net). The p-order learning predicts an optimal exponential coefficient rather than a fixed order number to extract moderate visual information from an image. The spatial attention learning aims to infer a weighted score that measures the importance of each local region, which can compact the image representations. To make ABP compatible with kernelized bilinear feature interaction, a crossed two-branch structure is utilized to combine the P-net and S-net. This structure can facilitate complementary information exchange between two different visual branches. The experiments on three widely used benchmarks, including fine-grained object classification and action recognition, demonstrate the effectiveness of the proposed method.

Mobile crowdsensing as a novel service schema of the Internet of Things (IoT) provides an innovative way to implement ubiquitous social sensing. How to establish an effective mechanism to improve the participation of sensing users and the authenticity of sensing data, protect the users’ data privacy, and prevent malicious users from providing false data are among the urgent problems in mobile crowdsensing services in IoT. These issues raise a gargantuan challenge hindering the further development of mobile crowdsensing. In order to tackle the above issues, in this paper, we propose a reliable hybrid incentive mechanism for enhancing crowdsensing participations by encouraging and stimulating sensing users with both reputation and service returns in mobile crowdsensing tasks. Moreover, we propose a privacy preserving data aggregation scheme, where the mediator and/or sensing users may not be fully trusted. In this scheme, differential privacy mechanism is utilized through allowing different sensing users to add noise data, then employing homomorphic encryption for protecting the sensing data, and finally uploading ciphertext to the mediator, who is able to obtain the collection of ciphertext of the sensing data without actual decryption. Even in the case of partial sensing data leakage, differential privacy mechanism can still ensure the security of the sensing user’s privacy. Finally, we introduce a novel secure multiparty auction mechanism based on the auction game theory and secure multiparty computation, which effectively solves the problem of prisoners’ dilemma incurred in the sensing data transaction between the service provider and mediator. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and efficient.

Abstract It is well known that the construction of publicly verifiable secret sharing scheme with high information rate is a challenge. The information rates of the existing schemes are generally less than one‐half; for this problem, we put forward a publicly verifiable secret sharing scheme with almost optimal information rate based on multilinear Diffie–Hellman assumption. First, we construct a knowledge commitment scheme by using multilinear map; on the basis of this scheme, we propose a publicly verifiable secret sharing scheme whose information rate is (m − 1)/m(The secret is (m − 1)‐dimensional vector), which is almost asymptotically optimal. Second, the public verifiability of the scheme is achieved by using the multiple linear property of a multilinear map. Again, under multilinear Diffie–Hellman assumption, we proved the security of our scheme. And we apply our publicly verifiable secret sharing scheme to public‐key encryption system skillfully. At last, the performance analysis results show the effectiveness and practicality of our scheme. Copyright © 2017 John Wiley & Sons, Ltd.

In the setting of secret sharing, a group of parties wish to run a protocol for recovering the secret when they input their shares. The security requirement include privacy, correctness and others. One desirable property is fairness, which guarantees that if either party gets the secret, then the other party does too. However, basic secret sharing schemes, verifiable secret sharing schemes and publicly verifiable secret sharing schemes do not satisfy the fairness. Moreover, fairness is a central objective of the rational secret sharing scheme. In this work, the authors study the fairness problem of secret reconstruction in a ( t , n ) secret sharing scheme. They use a new approach to achieve the fairness of the protocol. They first define the fair notion of secret sharing probabilistically. Using the definition, a fair ( t , n ) secret sharing scheme is proposed, its security and fairness are shown against three different attack types. The proposed scheme is an extension of basic model of secret sharing (Shamir's scheme) and the approach of fairness of Dov Gordon et al.(STOC2008). Theoretical analysis shows that the proposed scheme is more efficient.

In the setting of secret sharing, one desirable property is fairness, which guarantees that if there is a player getting the secret in the recovery phase, then each player participating to the reconstruction process does too. In this paper we study the fairness problem of secret reconstruction in a secret sharing scheme. We use a new approach to achieve the fairness of secret sharing. We first define the fairness of secret sharing probabilistically. Based on this definition, a fair secret sharing scheme is proposed, its security and fairness are shown against three different attack types and do not depend upon any unproven intractability assumption. Our scheme is an extension of Shamir's secret sharing scheme and the approach of fairness of Dov Gordon et al.(STOC2008). Theoretical analysis shows that our proposed scheme is more efficient.