Existing ciphertext-policy attribute-based encryption (CP-ABE) schemes primarily determine who is authorized to decrypt, yet they do not guarantee privacy once ciphertexts are decrypted. Differential privacy (DP) protects released results through noise perturbation, but its privacy budget ε is usually configured independently of access attributes, which hinders fine-grained multi-level privacy protection in hierarchical IoT data sharing. To bridge this gap, a single-encryption multi-level output framework is proposed, where an attribute-driven noise key derivation function establishes a chained mapping among access attributes, noise keys, and noise intensity, enabling one ciphertext to yield differently perturbed outputs at distinct ε levels for users with varying privileges. Authenticated encryption with associated data (AEAD) is further incorporated to enforce strong cross-version and cross-policy binding, preventing low-noise outputs from being forged or replayed across authorization levels. Theoretical analysis proves that the framework achieves IND-CPA confidentiality, ε-differential privacy and tamper-resistant noise binding, while experiments demonstrate superior privacy–utility trade-offs, multi-level adaptability, and tamper resistance, indicating that the framework is well suited for practical IoT data sharing scenarios.

Internet of Things (IoT) systems face escalating security threats as the number of connected devices surpassed 18.5 billion in 2024 and is projected to reach 39 billion by 2030. The dynamic and heterogeneous characteristics of IoT networks create vulnerabilities to sophisticated attacks. Existing trust management approaches struggle with these threats due to static graph construction, feature redundancy between direct and indirect trust, and inflexible fusion strategies. This paper proposes a Dual-Trust graph attention network-based malicious device detection method for IoT environments, named DTEM. The proposed scheme constructs dynamic temporal graphs based on real interaction history. Then it designs a completely decoupled dual-trust learning architecture. Finally it introduces a hybrid fusion mechanism. Overall, the improvements significantly enhance the detection accuracy of malicious devices in complex environments and adaptability to heterogeneous scenarios. Experimental results on the UNSW-NB15 dataset demonstrate that DTEM achieves an average F1-Score of 0.973 and ROC-AUC of 0.994 across three threat scenarios, outperforming traditional methods.

With the proliferation of IoT-based Smart Healthcare Systems (SHS), secure and accountable medical data sharing has become a critical challenge. In practice, hospitals, doctors, and specialists often need to collaborate across institutions, requiring fine-grained access to encrypted data while strictly protecting patient privacy and ensuring accountability in case of misuse. Existing inner-product functional encryption (IPFE) schemes, however, fall short in delegated access control and traceability of leaked keys. To address these gaps, we propose TD-IPFE. Built on bilinear groups and employing the dual system encryption technique, our scheme achieves selective chosen-ciphertext security (IND-td-CCA). By embedding user-specific trace identifiers and random exponents into the key structure, TD-IPFE enables flexible delegation with identity- and time-binding as well as efficient key traceability. Security analysis and experiments demonstrate that TD-IPFE ensures strong IND-td-CCA security with practical efficiency, making it well-suited for privacy-preserving medical data sharing in SHS.

Internet of Vehicles (IoV) has become the key technology to improve road safety and traffic efficiency. However, with the explosion of the number of vehicles and more frequent authentication, computing and communication costs increase. Nevertheless, most of the traditional IoV authentication protocols lack scalability and are vulnerable to physical attacks and internal attacks, so they are difficult to meet the needs of modern IoV environment. To address these issues, this paper proposes a hierarchical mutual authentication protocol for the IoV based on physical unclonable function (PUF) and fog computing. In this protocol, we design a three-layer architecture for IoV supported by fog computing, where the fog node (FN) acts as an intermediate authentication layer, managing a group of roadside units (RSUs) and sharing the computational tasks of the trusted authority (TA) to alleviate its burden. Moreover, PUFs are embedded in entity devices to encrypt sensitive parameters, preventing internal data leakage. Based on this architecture, our protocol implements both vehicle-to-infrastructure (V2I) authentication and group authentication. In the V2I authentication, the FN distributes session keys in bulk to a group of RSUs and multiple vehicles, significantly reducing the repetitive authentication overhead between vehicles and RSUs. In the group authentication, the RSU authenticates vehicles and distributes group key, thereby avoiding the need for frequent authentication. We have also implemented fast updates of session keys and group keys, independently completed by the FN and RSU, reducing reliance on the TA and enhancing key security. We have conducted both formal and informal security analyses of the proposed protocol and used the ProVerif tool to verify its security. The results demonstrate that the protocol meets the security requirements needed for IoV. The evaluation results shows that the proposed protocol can significantly reduce the computation and communication overhead, and improve the overall performance of the system.

In the dynamic moving process of vehicle clusters, there are several challenges, including inefficiencies, cross-domain trust issues and privacy leakage. To address these issues, we propose a blockchain-assisted group and cross-domain authentication key agreement, which implements distributed key management based on a threshold key sharing scheme, and realizes group authentication and group key distribution for vehicle clusters through the collaboration of roadside units (RSUs) and Key Generation Center (KGC). Meanwhile, a cross-domain trust chain is constructed based on blockchain to accomplish secure cross-domain authentication and key agreement without the participation of the original KGC, which solves the problem of trust deficiency and single-point vulnerability in the process of cross-domain communication. Finally, we employed Real-or-Random (ROR) formal security analysis and the ProVerif tool to verify that the proposed authentication scheme, the results show that the proposed scheme is secure and superior to existing schemes in terms of communication and computational overhead.

Integrated systems for underwater detection and communication (ISUDC) plays a pivotal role in improving sonar integration and efficiency and has become a key research focus. This article tackles the underwater doubly dispersive wireless channel (DDWC) by introducing a novel transmitter side waveform design and a receiver side channel estimation algorithm based on affine frequency division multiplexing (AFDM) within the ISUDC framework. At the transmitter we employ AFDM as the core signal and target minimization of weighted sidelobes in the wideband ambiguity function (WAF). We use numerical analysis to quantify coding effects on the WAF and apply optimized random phase perturbations in P4 encoding via particle swarm optimization (PSO) to enhance detection and improve time Doppler resolution. At the receiver we develop a sparse channel estimation method based on an affine Fourier dictionary, which uses pilot signals to estimate phase perturbations and exploits delay-Doppler sparsity to improve accuracy in dynamic underwater environments while reducing multipath interference. We also derive new bounds on the pairwise error probability (PEP) for underwater acoustic DDWC, including numerical lower bounds and Chernoff upper bounds. Simulations demonstrate that jointly optimizing waveform design and channel estimation reduces PEP and normalized mean-square error (NMSE), provides superior detection for consecutive identical coded symbols and yields an ideal “thumbtack” shaped WAF. The proposed framework delivers a reliable and efficient solution for ISUDC in complex underwater environments.

To address the limitations of traditional access control schemes in cloud storage environments—such as high computational and storage overhead, difficulty in supporting collaborative user access, and vulnerability to malicious collusion attacks—this paper proposes a Chinese Remainder Theorem (CRT)-based anti-collusion collaborative access control encryption scheme for cloud storage. Our solution introduces a dynamic authorization mechanism for collaborative nodes, enabling data owners to designate collaborative nodes in access policies and generate collaborative secret values. By integrating threshold secret sharing technology, these collaborative secret values are embedded into group user keys. Leveraging the modulus orthogonality of CRT, our scheme imposes two conditions for collaborative access: 1) the attribute sets of group users must satisfy the collaborative access policy; 2) The collaborative secret value shares held by group members must meet a predefined threshold. This design not only supports legitimate collaborative decryption but also automatically identifies cross-group collaboration as a collusion attack without requiring trusted third-party authorities. Security analysis demonstrates that our scheme ensures data confidentiality. Experimental evaluations show significant advantages in computation and storage efficiency compared to typical schemes such as CP-ABE and CP-WABE-CA.

The Internet of Vehicle (IoV) is a core component of smart city development. However, data interactions between IoV entities involve personal privacy, and once maliciously attacked, they may threaten the stable operation of the entire transportation system. Traditional authentication protocols suffer from high computational and communication overheads and are vulnerable to various threats, including physical attacks, entity impersonation, and replay attacks. Moreover, due to their reliance on centralized trusted authority(TA), traditional protocols are prone to single-point failures, especially when handling large-scale vehicle access. To address these challenges, this paper proposes a lightweight mutual authentication protocol based on physical unclonable function(PUF), which not only ensures vehicle anonymity and traceability but also supports a pseudonym update function after authentication. The protocol employs an architecture in which the main TA(MTA) is responsible for registration and data storage, while the sub-TA(STA) handles authentication, thereby effectively mitigating the risk of a single-point failure. Additionally, to counter the exposure of a large number of challenge-response pairs in traditional PUF-based authentication—making them susceptible to machine learning(ML)-based modeling attacks—this paper introduces a CUBE-PUF scheme based on digital Rubik’s Cube and random numbers. This approach enhances response unpredictability and randomness. We conduct both formal and informal security analyses of the proposed protocol and rigorously verify its security using the ProVerif verification tool. Furthermore, comparative evaluations with existing protocols demonstrate that our approach significantly reduces communication and computational overhead while offering enhanced security.

In quasi-static wireless channel scenarios, the generation of physical layer keys faces the challenge of invariant spatial and temporal channel characteristics, resulting in a high key disagreement rate (KDR) and low key generation rate (KGR). To address these issues, we propose a novel reconfigurable intelligent surface (RIS)-aided secret key generation approach using an autoencoder and K -means quantization algorithm. The proposed method uses channel state information (CSI) for channel estimation and dynamically adjusts the reflection coefficients of the RIS to create a rapidly fluctuating channel. This strategy enables the extraction of dynamic channel parameters, thereby enhancing channel randomness. Additionally, by integrating the autoencoder with the K -means clustering quantization algorithm, the method efficiently extracts random bits from complex, ambiguous, and high-dimensional channel parameters, significantly reducing KDR. Simulations demonstrate that, under various signal-to-noise ratios (SNRs), the proposed method performs excellently in terms of KGR and KDR. Furthermore, the randomness of the generated keys is validated through the National Institute of Standards and Technology (NIST) test suite.

Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint identification. To address this issue, we propose a few-shot RFFI (FS-RFFI) method based on interpolation metric learning and KNN (IntML-KNN). Specifically, the method first extends the dataset with data augmentation, and CutMix interpolation. Secondly, combining with metric learning to enhance the generalization capacity of the model. Finally, KNN algorithm is designed to realize device classification and detection. The proposed IntML-KNN method is verified on the commercial available LoRa dataset. The experimental results indicate that the proposed scheme exhibits strong classification and generalization performance in FS-RFFI. Meanwhile, the identification rate of the proposed IntML-KNN reaches 97.00% with only 10% samples. The codes of this paper can be downloaded from Github:https://github.com/happy-boy-cx/IntML-KNN.

Obtaining commercial value of private information from big data has become commonplace, which leads to misuse of information knowledge as well as violation of information owners’ rights, and curbing such behaviors has become a challenge. In this paper, we design an embedding scheme that can be applied to privacy protection of secret information, i.e., embedding confidential information such as copyright as secret information in cover images. The secret information is divided into multiple clusters, encrypted and compressed through the use of multiple-zone folding method to optimize the embedding efficiency and minimize the distortion caused by the embedding process, it realizes the privacy feature of traceability and security protection of secret information in circulation. Evaluated by security analysis and experimental results, this proposed scheme achieves IND-CPA high information security level for information protection. Compared with the state-of-the-art scheme, the computational complexity of this proposed scheme isO(Y) (Ydenotes the total number of pixels), at least 6 bits of information can be embedded per pixel which improves the efficiency of embedding. In terms of the impact on the quality of cover image information after the embedding of secret information, it has better performance, and improves the manageable traceability of information.

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.

Summary Knowledge graphs contain a large amount of entity and relational data, and graph neural networks, as a class of efficient graph representation techniques based on deep learning, excel in knowledge graph modeling. However, previous neural network architectures for the most part only learn node representations and do not fully consider the heterogeneity of data. In this article, we innovatively propose a privacy attack framework based on IoT, PAFI, which is able to classify entities and relations, learn embedding representations in multi‐relational graphs, and can be applied to some existing neural network algorithms. Based on this, a fine‐grained privacy attack model, FPM, is proposed, which can perform attack operations on multiple targets, achieve selectivity of target tasks, and greatly improve the generalization ability of the attack model. In this article, the effectiveness of PAFI and FPM is demonstrated by real network datasets, and compared with previous attack methods, both of which achieve good results.

The Internet of Vehicles (IoV) is a specific instance of the Internet of Things (IoT) in the transportation field, driven by application requirements, such as intelligent traffic services and automatic vehicle control, can improve road safety and enhancing transmission efficiency. However, highly open networks tend to bring more security threats, and secure authentication becomes an important guarantee for reliable communication. Traditional IoT authentication and key agreement methods are costly, inefficient, and rely on the third-party trusted institutions, making them unsuitable for direct application in IoV systems. To meet the security authentication needs of IoV, and improve authentication efficiency and anonymity, this article proposes a verifiable commitment-based mutual authentication and key agreement protocol, called mutual authentication and key agreement protocol based on verifiable commitment (VC-MAKA). In VC-MAKA, we construct a verifiable commitment scheme where the verifier can verify the committed secret. Furthermore, based on this verifiable commitment scheme, we implement secure authentication and session key agreement, allowing vehicles to freely negotiate secure session keys and achieving conditional anonymous protection. Additionally, the proposed VC-MAKA also achieves rapid session key updates, enhancing the security of the session keys. We have conducted formal and informal security analysis, and the results show that VC-MAKA meets security requirements, such as mutual authentication, anonymity, traceability, and untraceability. Moreover, we have used the ProVerif tool for security experiment and performance comparison analysis, and the results indicate that compared to other schemes, the VC-MAKA protocol offers higher security and better efficiency.

Fair data trading is a complex process that is often hindered by a fundamental issue of trust between data suppliers and collectors. This mistrust can lead to an impasse: data collectors hesitate to pay upfront without the data in hand, while data suppliers hold back the data until they are assured of payment. Though enlisting a trusted third party may mitigate these issues, it also presents distinct security challenges that must be carefully considered. Observing that the blockchain technique has great potential to improve security, efficiency, and transparency of data trading, we present a blockchain-based fair data trading scheme, called BFFDT, which allows the data seller trade its data in part with an interested purchaser through a smart contract for revenue. In BFFDT, the data publisher first generates the authenticated tags based on the data fields and corresponding attribute values, then encrypts the corresponding attribute values individually and generates a dynamic Merkle hash tree (D-MHT) to ensure the consistency of the attributes and attribute values. In addition, we design an innovative pairing-based proxy re-encryption mechanism to transmit the ciphertext of a symmetric key to the purchaser’s public key via a re-encryption key without any third-party intermediary, and verifies the re-encryption key using the verifiable commitment. Furthermore, the BFFDT is formally proven to be secure against the deceitful actions of both the fraudulent seller and buyer, and the experimental outcomes further confirm that BFFDT offers high efficiency and practical applicability.

Internet of Vehicles (IoV) is a critical component of the transportation field, which can greatly facilitate the current transportation system. Meanwhile, more and more vehicles connect to the IoV and the security and privacy need to be guaranteed. Traditional authentication protocols based on bilinear pairs are computatively heavy and difficult to protect user identity and privacy in IoV environment. In addition, most existing protocols only consider the authentication between vehicles and infrastructure, but not consider between vehicles and vehicles, as well as single point of failure in the traditional single trusted authority (TA). To address these issues, this article proposes two lightweight mutual authentication protocols (MAPs) based on physical unclonable function (PUF) and multi-TA. The first protocol named V2I-MAP and is applied to vehicle-to-infrastructure (V2I) communication. The second is named V2V-MAP and is applied to vehicle-to-vehicle (V2V) communication. The protocols solve the interference of noise on PUF signals by fuzzy extractor, reduce the communication overhead and computation overhead of vehicles by utilizing PUF’s lightweight computation characteristics, deal with the problems of impersonation attack with the help of the unclonable characteristics of PUF, and work out single TA single point of failure problems with the multi-TA model. Finally, the security analysis and informal security analysis of the proposed protocols are demonstrates that the proposed protocols meet the security requirements of the IoV system. ProVerif is used to verify the security of the protocols. Performance analysis shows that the protocols can reduce the communication and computation overhead than the comparable protocols.

The challenge of classifying multimodal remote sensing images has garnered significant interest in light of the growing diversity of remote sensing image data modalities. Current studies primarily concentrate on increasing the classification task’s accuracy by improving the fusion strategy or incorporating auxiliary architectures. However, there is currently a lack of research in the area of adversarial attack for multimodal remote sensing image classification models as compared to the study of unimodal classification models. To overcome this issue, our research firstly investigates the adversarial robustness of multimodal remote sensing image classification under different fusion strategies, which adopts the improved classical adversarial attack methods to test the adversarial robustness of multimodal remote sensing image classification model architectures with three different fusion strategies; Then a new multimodal adversarial attack method is proposed for the multimodal model, which adopts balanced perturbation loss and cooperative adversarial loss, in which the balanced perturbation loss is used to balance the level of perturbation of different modalities, and the cooperative adversarial loss is used to reduce the conflict of different modality perturbations on the classification result. By combining balanced perturbation loss and cooperative adversarial loss to attack multimodal models, the cooperation between modalities is continuously optimized.Finally, the study demonstrates the weak adversarial robustness of the multimodal remote sensing image classification model, which robustness is easily influenced by the fusion strategies and the attack methods. Additionally, a better attack effect is obtained by the multimodal multiloss cooperative adversarial attack method proposed in this paper.

In Industry 5.0, there are increasing demands for group communication with low energy consumption and high communication efficiency from a great number of Internet of Things (IoT) devices. However, group communication is still exposed to various security risks. Although some cryptographic schemes have been devised to facilitate secure group communication, the existing schemes generally rely on a trust authority to periodically issue certificates and have led to various issues, such as failing to support anonymity and flexible key management, and cannot resist the single point of failure. Therefore, in this work, leveraging Chebyshev chaotic maps and blockchain, an anonymous authentication and key management scheme is proposed to provide secure and efficient group key generation and management for mutual authentication between communication entities. The scheme exploits the blockchain to save the key materials associated with IoT devices, thereby it ensures data privacy and provides a secure environment for communication. The scheme also employs the Chebyshev polynomial to generate a group key for the IoT devices within a group, and later the group members holding the same group key can use it for secure communication. The formal and informal security analysis demonstrates that the proposed scheme can meet the security and flexible key management requirements. The detailed performance analysis shows that the proposed scheme has acceptable computation and communication energy consumption and provides superior security in comparison with existing schemes.

Shared gradients are widely used to protect the private information of training data in distributed machine learning systems. However, Deep Leakage from Gradients (DLG) research has found that private training data can be recovered from shared gradients. The DLG method still has some issues such as the “Exploding Gradient,” low attack success rate, and low fidelity of recovered data. In this study, a Wasserstein DLG method, named WDLG, is proposed; the theoretical analysis shows that under the premise that the output layer of the model has a “bias” term, predicting the “label” of the data by whether the “bias” is “negative” or not is independent of the approximation of the shared gradient, and thus, the label of the data can be recovered with 100% accuracy. In the proposed method, the Wasserstein distance is used to calculate the error loss between the shared gradient and the virtual gradient, which improves model training stability, solves the “Exploding Gradient” phenomenon, and improves the fidelity of the recovered data. Moreover, a large learning rate strategy is designed to improve model training convergence speed in‐depth. Finally, the WDLG method is validated on datasets from MNIST, Fashion MNIST, SVHN, CIFAR‐100, and LFW. Experiments results show that the proposed WDLG method provides more stable updates for virtual data, a higher attack success rate, faster model convergence, higher image fidelity during recovery, and support for designing large learning rate strategies.

Benefiting from the development of smart urban computing, the mobile crowd sensing (MCS) network has emerged as momentous communication technology to sense and collect data. The users upload data for specific sensing tasks, and the server completes the aggregation analysis and submits to the sensing platform. However, users’ privacy may be disclosed, and aggregate results may be unreliable. Those are challenges in the trust computation and privacy protection, especially for sensitive data aggregation with spatial information. To address these problems, a verifiable location-encrypted spatial aggregation computing (LeSAC) scheme is proposed for MCS privacy protection. In order to solve the spatial domain distributed user ciphertext computing, firstly, we propose an enhanced-distance-based interpolation calculation scheme, which participates in delegate evaluator based on Paillier homomorphic encryption. Then, we use aggregation signature of the sensing data to ensure the integrity and security of the data. In addition, security analysis indicates that the LeSAC can achieve the IND-CPA indistinguishability semantic security. The efficiency analysis and simulation results demonstrate the communication and computation overhead of the LeSAC. Meanwhile, we use the real environment sensing data sets to verify availability of proposed scheme, and the loss of accuracy (global RMSE) is only less than 5%, which can meet the application requirements.

In order to reap the full scale of benefits of millimetre wave (mmWave) massive multiple‐input multiple‐output (MIMO) systems, the design of antenna arrays at the transmitter or receiver becomes more critical due to the propagation characteristic at mm‐frequencies. In this study, the authors investigate the performance of two types of antenna array, namely uniform rectangular planar array (URPA) and uniform cylindrical array (UCYA). The channel behaviour is presented in full‐dimensional mmWave propagation conditions by considering both the azimuth and elevation dimensions. The squared inner product and singular value spread are studied for URPA and UCYA configurations, these properties reveal the effective interference and channel's stability of antenna array. The authors also evaluate the achievable rate with the equal power allocation and water‐pouring power allocation schemes. Simulation results show that under the same system configurations, the performance that includes the radiation pattern, the channel eigenvalue distribution, the effective interference, and the achievable rate, of UCYA configuration always outperforms that of URPA configuration. Therefore, it can be concluded that in three‐dimensional propagation environments, the UCYA configuration is especially appealing for mmWave MIMO systems.