The rapid growth of intelligent manufacturing within the Industrial Internet of Things (IIoT) has highlighted the importance of quality of service (QoS) as a key metric for system performance and reliability. Due to its dynamic and heterogeneous nature, accurate QoS prediction is essential but remains difficult under data silos, privacy constraints, and limited trust among industrial entities. To overcome these limitations, this article proposes a robust and secure multiparty collaborative (RSMC) QoS prediction scheme for IIoT service recommendation. RSMC leverages a federated learning architecture with a secure aggregation strategy, facilitating joint model training across factories without exposing raw data. Specifically, a hybrid local time-aware prediction model integrating a temporal convolutional network with a bidirectional simple recurrent unit is employed to capture long-term dependencies and nonlinear dynamics in QoS time series. To ensure the security of collaborative training, a robust functional encryption mechanism is introduced to achieve secure parameter aggregation and effectively resist inference and collusion attacks. Experiments on real QoS datasets show that the proposed scheme achieves superior prediction accuracy compared with mainstream approaches, while preserving privacy and enabling large-scale collaboration.

Maximum distance separable (MDS) codes have significant combinatorial and cryptographic applications due to their certain optimality. Generalized Reed-Solomon (GRS) codes are the most prominent MDS codes. Twisted generalized Reed-Solomon (TGRS) codes may not necessarily be MDS. It is meaningful to study the conditions under which TGRS codes are MDS. In this paper, we study a general class of TGRS (ATGRS) codes which include all the known special ones. First, we obtain another expression of the inverse of the Vandermonde matrix. Based on this, we further derive an equivalent condition under which an A-TGRS code is MDS. According to this, the A-TGRS MDS codes include nearly all the known related results in the previous literatures. More importantly, we also give three constructions to obtain many other classes of MDS TGRS codes with new parameter matrices. In addition, we present a new method to compute the inverse of the lower triangular Toplitz matrix by a linear feedback shift register, which will be very useful in many research fields.

The widespread deployment of 5G networks has accelerated the development of Internet of Vehicles (IoV), laying the foundation for the development of intelligent transportation systems (ITSs) in future 6G networks. The unprecedented intelligence of 6G ITS is expected to enable the automation of vehicles, seamless collaboration, and intelligent management. High-volume information sharing plays a crucial role in this case, and its security and reliability becoming important cornerstones for 6G ITS. Without adequate security protection and trusted environments may result in unreliable and inefficient network services. In this article, we propose a reputation management-based blockchain, Info-Chain, for secure, trustworthy, and privacy-preserving information sharing in 6G ITS. Furthermore, to accommodate the distributed traffic environment in 6G ITS, we construct a novel consensus mechanism for Info-Chain, denoted as Proof of Reputation and sum (PoRs), which combines reputation with traffic environmental factors as competitive conditions. Finally, we propose a dynamic incentives and punishments mechanism that utilizes evolutionary game theory to guide vehicles to actively and honestly participate in information sharing. The security analysis shows that the proposed mechanism is capable of resisting common attacks, and the simulation result demonstrates that the proposed scheme not only enables efficient and secure information sharing in 6G ITS but also encourages vehicles to actively participate in information sharing.

Generalized Feistel structures are widely used in the design of block ciphers. In this paper, we focused on retrieving impossible differentials for two kinds of generalized Feistel structures: CAST256-like structure with Substitution-Permutation (SP) or Substitution-Permutation-Substitution (SPS) round functions (named CAST256SP and CAST256SPS , respectively) and MARS-like structure with SP/SPS round function (named MARSSP and MARSSPS , respectively). Known results show that for bijective round function, CAST256-like structures and MARS-like structures have ( m2−1 ) and ( 2m−1 ) rounds impossible differentials, respectively. By our observation, there existed ( m2+m ) rounds impossible differentials in CAST256SP and ( 3m−3 ) rounds impossible differentials in MARSSPS (this result does not require the P layer to be invertible). When the diffusion layer satisfied some special conditions, CAST256SPS had ( m2+m−1 ) rounds impossible differentials and MARSSPS had ( 3m−3 ) rounds impossible differentials.

This paper first gives a regularity theorem and its corollary. Then, a new construction of generating hard random lattices with short bases is obtained by using this corollary. This construction is from a new perspective and uses a random matrix whose entries obeyed Gaussian sampling which ensures that the corresponding schemes have a wider application future in cryptography area. Moreover, this construction is more specific than the previous constructions, which makes it can be implemented easier in practical applications.

Recently, Liuet al. proposed an efficient privacy-preserving outsourced calculation framework with multiple keys. The building blocks of this framework are a modified homomorphic encryption scheme and a basic interactive protocol for computing the sum of two encrypted dates across two domains. We found that there is a mistake in the parameter setting of their modified homomorphic encryption scheme and a security leak in the basic interactive computation protocol. This security leak enables an adversary to compromise the privacy of users, which are claimed to be protected. By our analysis, we suggest that the proposed framework, without any modification, should be avoided for use in privacy-preserving outsourced computation, and a similar scheme should be carefully designed in the future.

Abstract Wireless sensor networks (WSNs) have been widely used in different fields, such as battle field, environment monitoring, and intrusion detection, where amount of nodes are deployed. There are many factors make sensor nodes being more vulnerable to be attacked and compromised. In order to address the issue, many different schemes and methods are proposed. Trust evaluation and trust management play an important role in detecting the malicious nodes. In this paper, we propose a trust management system for clustered WSNs monitoring the sensor nodes' behaviors and evaluating their trust values. This system employs a hash algorithm for generating identify labels for sensor nodes to distinguish external attackers from normal nodes and dynamically manages the trust value of each node to detect the compromised nodes based on the trust evaluating model, which is based on beta density function. The simulation results show that our scheme can detect the malicious nodes quickly, which prevents clustered WSNs from external attacks and internal compromised nodes' attacks. Copyright © 2015 John Wiley & Sons, Ltd.

We propose a novel passively secure group key exchange GKE scheme via learning with errors LWE. Then, we extend it to a GKE scheme via ring-learning with errors. We show the security analysis of our GKE schemes based on the LWE assumption and the security property of secure sketch in the random oracle model. One of our innovative points is a simple and practical robust extractor that serves as the key derivation function. Based on our GKE scheme, we build a group authenticated key exchange scheme and then prove its security under the LWE assumption and the secure property of secure sketch in the random oracle model. The proposed scheme not only provides authenticated key exchange security and mutually authenticated security but also needs only two round communications to negotiate a shared session key. Copyright © 2015John Wiley & Sons, Ltd.

Community detection is important in understanding the structures and functions of complex networks. Many algorithms have been proposed. The most popular algorithms detect the communities through optimizing a criterion function known as modularity, which suffer from the resolution limit problem. Some algorithms require the number of communities as a prior. In this paper, a non-modularity based compression optimization algorithm for community detection is proposed without any prior knowledge, which is efficient and is suitable for large scale networks.

Cloud computing technology is an emerging technology for various types of vast information to be processed in the data centers to overcome the serious shortage of resource such as space, power and cost, etc. Along with the development of cloud computing, security of cloud computing is becoming more and more critical. In this paper, we aim to propose a set of secure and efficient authenticated multi-party key agreement protocols based on the hierarchical identity-based cryptography (HIBC) for private cloud. For different scenarios, we design loop-level and cascade-level authentication protocols among users in a private cloud of hierarchical structure, respectively. The new multi-party protocols are supposed to be more efficient and secure than the other existing solutions.

The recently developed Compressed Sensing (CS) theory has made the super-resolution of spectrum estimation possible. In this paper, we exploit the joint sparsity of received signals to develop a new Compressive Direction-of-Arrival Estimation approach via a new Regularized Multiple Measurment FOCal Underdetermined System Solver (RMM-FOCUSS) Algorithm. It can overcome the resolution limitation of traditional spatial energy spectrum estimation algorithm, such as MUSIC algorithm, and present more accurate estimation of direction of multiple sources when there are a few numbers of antenna units. Some experiments are taken to validate the performance of our proposed method.

A novel clustering algorithm called Immune Memetic Clustering Algorithm (IMCA) is proposed in the paper. IMCA combines Immune Clone Selection and Memetic algorithm; Two populations are used in the evolutionary process. Clone reproduction and selection, Memetic mutation, crossover, individual learning and selection are adopted to evolve the two populations. After watershed proceeding, extracting the texture features of an image and encoding them with real numbers, IMCA is used to partition these features, and the final segmentation result is obtained. This approach is applied to segment three types of images, including artificial synthetic texture images, natural images, and SAR images, the experimental results show the effectiveness of the proposed algorithm.

In this paper, by denning the Lee distance and the Lee weight of linear codes over the ring R = F2+uF2+ u2F2, we construct a Gray map which is both an isometry and a weight-preserving map from Rnto F23n. In addition, using the proposed Gray map, we give an existence condition of quantum codes which are derived from cyclic codes over R with the Lee metric.