Blockchain, as a rapidly developing technology in nowadays, involves multi-party collaboration scenarios. However, as the number of users grows, security issues in blockchain systems also increase, driving the need for features such as collusion resistance and traceability. To meet the needs of multi-party collaboration on the blockchain, we propose a blockchain-based collusion-resistant and traceable broadcast encryption scheme. On the one hand, the traitor tracing scheme is adopted to effectively enable accountability for malicious users. On the other hand, the SM2 public key encryption algorithm is deployed to satisfy high security requirement with relatively low computational costs. Security analysis demonstrates that the proposed scheme has the same level of security as the SM2 algorithm. Performance evaluation shows that the proposed scheme is superior to the relevant schemes and maintains functionalities such as collusion-resistant and traitor tracing.

With the rapid development of smart grid technologies, ensuring secure and efficient collaboration among distributed grid units has become a significant challenge. However, existing works suffer from the key escrow problem and single points of failure. Moreover, decentralized alternatives often lack efficient mechanisms to prevent rogue nodes from manipulating collaborative decisions. This paper proposes a certificateless multi-signature scheme named MagicCMS, which is based on a magic square structure. By integrating element-based node collaboration through magic squares, MagicCMS ensures signature generation only when authorized node groups meet predefined constraints. This enhances security against rogue nodes. Additionally, the system utilizes a decentralized key management solution leveraging blockchain technology, thereby eliminating single points of failure. A rigorous security analysis demonstrates that MagicCMS resists both Type-I and Type-II adversaries and provides protection against rogue-key attacks. Performance evaluations show significant improvements in signature verification efficiency compared to existing schemes. In large-scale scenarios, verification computational costs are reduced by up to 73%.

The widespread deployment of smart grids has brought significant convenience to residential life. However, it also presents key challenges for data aggregation in smart grids.M1: The hierarchical structure of smart grid consumers (e.g., residential, industrial, commercial) requires differentiated allocation strategies to meet varying electricity demands while protecting consumer privacy.M2: The existing methods, such as superincreasing sequence, often face efficiency challenges, particularly when dealing with multidimensional data.M3: Smart meters continuously collect diverse power consumption data containing users' private information, which is vulnerable to tampering or loss, compromising data integrity and impacting power dispatch decisions. To address these challenges, this paper proposes a blockchain-based, efficient, verifiable, and weighted multidimensional data aggregation scheme for smart grids. First, a novel five-layer cloud-chain-assisted multiscenario data security aggregation model is proposed. Second, instead of using superincreasing sequences, we introduce the Chinese Remainder Theorem to process multidimensional data, thereby reducing communication complexity. Additionally, the property of quadratic reciprocity is leveraged to enhance the decryption method of the Paillier cryptosystem, reducing computational overhead. A weighted aggregation function is implemented to accurately aggregate data based on different user attributes. Furthermore, we propose two sample configurations to address distinct scenario requirements. Security analysis and experimental results demonstrate that the proposed scheme meets practical requirements in terms of both security and efficiency.

With the rise of blockchain and next-generation networks (NGNs) technologies, privacy and security issues have become the main bottlenecks of the conventional network architecture solution. The asymmetric group key agreement (AGKA) protocol is a practical and effective cryptographic primitive in achieving decentralized authentication for zero-trust architecture. However, conventional AGKA protocols suffer from collusion attack issues and cannot provide privacy-preserving authentication with traceability. This paper aims to develop novel approaches for privacy-preserving authentication and secure data sharing for zero-trust architecture. First, we construct an AGKA protocol, which is so far the first AGKA protocol immune to the collusion attack. Next, we develop an anonymous authentication (AA) scheme that supports the anonymity and traceability properties simultaneously. Moreover, benefiting from the immutability of blockchain, the identity of malicious users can be detected. Third, we design a conditional data sharing (CDS) scheme by means of the secret sharing scheme, which could guarantee the confidentiality and reliability of the users' data from multi-cloud. Finally, we conduct security and performance evaluations, which show that the developed schemes can resist different kinds of attacks with high performance.

As a cloud storage side in the cloud-edge-terminal collaboration, which empowers the artificial intelligence of things (AIoT), the accuracy of data retrieval and data privacy in the cloud can significantly impact the quality of service in AIoT. Typically, the cloud facilitates data sharing through keyword-based private information retrieval (PIR). However, these keywords may contain the privacy of patients, causing the server to gain patients privacy during database retrieval, resulting in privacy exposure. Concurrently, malicious users seek to access more datasets than those corresponding to the keywords. It is worth to consider the construction of a secure and private retrieval system in AIoT. To protect the privacy of AIoT, this paper proposes two multi-keyword PIR schemes: the fuzzy multi-keyword PIR scheme and the fine-grained flexible multi-keyword PIR scheme. The fuzzy multi-keyword PIR scheme utilizes the proposed batch oblivious pseudo-random function (B-OPRF) based on OTEn1 to implement the batch search. If one of the n keywords in a dataset matches a requested keyword, the corresponding datasets are returned to the user, achieving fuzzy retrieval. The fine-grained flexible multi-keyword PIR scheme incorporates the proposed batch flexible OPRF (BF-OPRF) algorithm, wherein k out of the n keywords in the dataset must match the k requested keywords from users for the corresponding datasets to be returned to the user. Additionally, the cloud server may tamper with the data, and the correctness of the data is periodically verified using a verifiable mechanism. The effectiveness and performance of the proposed schemes are validated through experiments and theoretical analysis.

With the rapid evolution of wireless communication technology, smart homes have significantly improved the quality of peoples daily lives by taking advantage of the low latency and high transmission rates of 6G communication technology. Users can now conveniently manage the consumer electronics remotely. However, in the pratical smart home scenarios, the users and consumer electronics communicate with each other through an open public channels where charted and uncharted security risks and privacy vulnerabilities exist. To protect users confidential data from malicious interception and modification, diverse authentication protocols have been proposed so far. However, existing protocols often suffer from efficiency issues or vulnerabilities to known attacks. To address these challenges, this paper proposes a novel three-factor ECC-based anonymous authentication protocol. The protocols security properties can be rigorously proven using the formal analysis under the ROR model. Subsequently, the resistance to numerous types of attacks, including man-in-the-middle and replay attacks, can be demonstrated through informal analysis and the AVISPA verification process. Finally, the protocol is compared with the state-of-the-arts and the results show that the protocol strikes a good balance between security and efficiency and is well suited for smart home environments.

Recently, Industrial Internet of Things (IoT) has experienced significant growth and has garnered widespread interest. With industrial IoT, remote users can link and manage various sensing devices and gather instantaneous data from devices, thereby improving efficiency and productivity in industrial settings. However, challenges with existing authentication protocols in the Industrial IoT environment have been identified. To address these shortcomings, we propose a high-availability authentication and key agreement protocol for IoT-based devices, which reduces computational and communication overheads. In addition, our protocol offers privacy protection to users as the data message is transmitted by the users' pseudonym, helping to conceal their real identity. Furthermore, we also consider device dynamic updates in our proposed protocol. Security proof and analysis are provided to prove that our proposed protocol can withstand known attacks. In addition, performance analysis shows that our proposed protocol is a more efficient option than other related protocols.

Industry 5.0 is more focused on sustainability, people-centredness, and resilience. The Industrial Internet of Things (IIoT) enable real-time monitoring of equipment status through sensors deployed in dangerous environments to reduce the probability of hazards. However, the devices are vulnerable to malicious attackers or can be tampered with during the transmission of information. Security has therefore become a major concern in the IIoT. In this article, we propose a privacy-preserving multinode collaborative decision-making scheme in IIoT to ensure the validity and integrity of data under the IIoT. This collaborative decision-making solution primarily uses voting to enable accurate monitoring of sensing devices. The scheme is designed based on an efficient aggregate signature and gives a formal proof of security based on the computational Diffie–Hellman problem (CDHP). The final performance analysis phase gives a detailed presentation of the computational overhead of each phase of the protocol.

Nowadays, with the purpose of fulfilling the increasing demands for emerging extensive and ubiquitous accessibility to modern intelligent transportation systems (ITS), the conventional vehicle-to-everything (V2X) paradigms are progressively evolving into the Internet of vehicles (IoVs). As of now, substantial enhancements in IoV technologies have been witnessed, particularly in the era of vehicular data secure exchange and user privacy protection. On the other hand, unmanned aerial vehicles (UAVs) are envisioned to provide scalable and adaptive stereoscopic service coverage for IoV implementations. However, the existing approaches intended for the common scenarios mainly rely on the well-established local IoV infrastructures, whereas the specific infrastructure-less IoVs with dysfunctional edge facilities have not been properly investigated. Additionally, the related UAV-assisted IoV schemes still fail to independently perform the secure vehicular data transmission but rather as the auxiliary strategies due to the dependence on the assistance of local edge deployment. Therefore, the practical requirements of reliability and scalability in real-world IoV circumstances cannot be satisfied. In this paper, an efficient UAV certificateless group authentication mechanism is developed in order to facilitate the secure data transmission of infrastructure-less IoV. The proposed design deploys the tethered UAV (TUAV) as the specific mobilized base station so that the active edge IoV infrastructure is not needed. The security analysis regarding the key security features are presented first, followed by the performance evaluation. According to the comparison results with the existing designs, improvements in terms of computational cost and communication overhead for different phases can be demonstrated.

In recent years, growing research interest from both industry and academia has been aroused to the vehicular networks, which is regarded as the fundamental component of the modern intelligent transportation system (ITS). Lots of remarkable research outputs with respect to secure vehicular data interactions and user privacy preservation has been witnessed. However, the existing schemes all focus on the common vehicular communication scenarios where facilities are deployed, whereas the secure data exchange in the abnormal infrastructure-less vehicular environment has not been properly investigated. To deal with unpredictable abnormal situations caused by artificial or natural disasters such as earthquakes and floods, a distinctive vehicle-assisted aggregate authentication mechanism for infrastructure-less vehicular networks is presented in this paper. With assistance from the neighboring vehicles, the homomorphic signature involving all requesting vehicles is generated and forwarded to the remaining functional RSUs. Meanwhile, vehicular group communication among the validated entities is enabled. Additionally, the fault-tolerant verification method is adopted such that the ineffective entities can be easily distinguished and removed without interfering with other requesting vehicles. The security proofs and discussions regarding vital security properties are presented, while the performance analysis follows. Compared with the state-of-the-art, advantages in terms of security and performance properties can be proved.

With the popularity of image editing tools, the originality and information security of images are facing serious threats. The most common threat is splicing forgery that copies a part of the area from one donor image to the acceptor one. Some research works were proposed to protect the image originality, whereas they are still difficult to apply in practice. There are two main reasons: (a) very limited data for learning models; (b) huge attribute differences between the donor and acceptor images. We propose two novel tasks to conquer the above challenges: Synthetic Adversarial Networks (SANs) and Hybrid Dense U-Net (HDU-Net). SAN finds the most secluded position for inserting tampered areas in an image by learning the association between scenes and objects, and can enlarge the original small data set by more than 40 times. We call the data set SF-Data generated by SAN. We combine the dense U-Net that detects the differences of the essential attributes of image with four spaces containing more available feature information to propose HDU-Net. Then, the synthetic data set SF-Data are used to train HDU-Net. We perform various attack experiments on several public data sets to demonstrate the effectiveness and robustness of our method.

While cloud computing services such as cloud storage are fairly mature, it remains challenging to design efficient and secure cryptographic schemes to facilitate fine-grained access control and achieve other features. For example, existing ciphertext-policy attribute-based encryption schemes do not generally have in-place limits on the number of access or access duration, which can consequently be exploited to perform economic denial of sustainability and other attacks. In addition, improving system efficiency can be challenging in large-scale operations. Thus, in this paper, we present a blockchain-based access control with k $k$ -times tamper resistance. Our proposed approach allows one to set and enforce quota/limits on the number of accesses allowed for each user, whose integrity is ensured using blockchain. In addition, our proposed approach also allows multiple attribute authorities to coexist and work together with a central authority to facilitate secret key distribution and improve system efficiency. We then evaluate the security and the efficiency of our proposed approach to demonstrate its utility.

Cognitive computing over big data brings more development opportunities for enterprises and organizations in industrial informatics, and can make better decisions for them when they face data security challenges. To satisfy the requirement of real-time data storage in industrial Internet of Things (IoT), the remote unconstrained storage cloud is usually used to store the generated big data. However, the characteristic of semitrust of the cloud service provider determines that the data owners will worry about whether the data stored in cloud computing has been corrupted. In this article, a secure storage auditing is proposed, which supports efficient key updates and can be well used in cognitive industrial IoT environment. Moreover, the proposed basic auditing can be extended to support batch auditing that is suitable for multiple end devices to audit their data blocks simultaneously in practice. In addition, a hybrid data dynamics method is proposed, which employs a hash table to store the data blocks and uses a linked list to locate the operated data block. Compared with previous methods, the data block location time in the proposed data dynamics can be reduced by 40%. The security analysis results demonstrate that the proposed scheme can be proved to be correct, and is secure under computational differ-hellman (CDH) and discrete logarithm (DL) assumptions.

Smart mobile devices are one of the core components of the wireless body area networks (WBANs). These devices shoulder the important task of collecting, integrating, and transmitting medical data. When a personal computer collects information from these devices, it needs to authenticate the identity of them. Some effective schemes have been put forward to the device authentication in WBANs. However, few researchers have studied the WBANs device authentication in emergency situations. In this paper, we present a novel system named emergency medical system without the assistance of doctors. Based on the system, we propose an identity‐based fast authentication scheme for smart mobile devices in WBANs. The scheme can shorten the time of device authentication in an emergency to achieve fast authentication. The analysis of this paper proves the security and efficiency of the proposed scheme.

In this paper, a reliable and energy efficient cooperative relaying (REECR) scheme was proposed for multi-hop communications in wireless sensor networks (WSNs). The scheme investigates node cooperation in conjunction with power control to conserve transmission energy and improve communication reliability in the presence of fading. By using a set of relay nodes that are located between transmitter and receiver to assist packet delivery, the scheme is capable of reaping both spatial diversity gains and path-loss savings. Local channel information is utilized in the relay selection process and the power control to reduce the energy consumption of data packet transmission. Moreover, a cooperative automatic repeat request (ARQ) is incorporated, whereby an intermediate node is chosen to help packet retransmission. Our simulations and analyses show that the proposed scheme improves energy efficiency, packet delivery ratio and extends nodes' battery lifetime.

This study examines the relationship between the lifestyles of Chinese consumers and the adoption of mobile services. Based on a sample from 313 respondents from the biggest city in central China, the results show that consumers with different lifestyles have different preferences over a number of identified mobile services. It is found that there are some lifestyle factors, such as the investment consciousness and the financial contentment consciousness, having significant negative impact on the adoption of office/learning tools on mobile devices. Furthermore, Chinese consumers are clustered into four lifestyle segments by two dimensions: the quality-awareness fashionable dimension and the economical dimension. The findings demonstrate that the quality-awareness fashionable dimension has stronger impact than the economical dimension toward the adoption of all the five types of mobile services.