ABSTRACT To address the challenges of coarse‐grained access control, collusion attack risks, and massive data storage issues in cross‐departmental traffic data sharing within Intelligent Transportation Systems (ITS) scenarios, this study proposes an Attribute‐Based Conditional Proxy Re‐Encryption (AB‐CPRE) scheme integrated with blockchain technology. This scheme employs a conditional proxy re‐encryption mechanism to achieve fine‐grained access control based on device attributes and access policies, thereby defending against collusion attacks by proxy nodes and malicious users. By combining the distributed ledger of blockchain and IPFS's distributed storage of IPFS, a verifiable system is constructed that includes ciphertext hashes, a complete set of device attributes, and operational conditions. This ensures data integrity while reducing the computational and storage pressure on edge servers. Security analysis demonstrates that the scheme satisfies adaptive IND‐CCA security under the standard model, and performance evaluation indicates significant improvements in computational efficiency and communication overhead compared with similar schemes.

With the continuous expansion of industrial internet of things (IIoT) applications, secure cross-domain authentication has emerged as a critical technical challenge. Traditional authentication schemes based on trusted third parties suffer from single point of failure risks, while blockchain-based decentralized approaches incur excessive computational overhead. Both methods share a common deficiency: authentication nodes must repeatedly execute authentication procedures at each target domain’s gateway when performing cross-domain authentication. This repetitive mechanism significantly increases resource consumption and limits the system’s ability to satisfy strict real-time requirements. To address these challenges, this paper proposes a lightweight and reusable cross-domain authentication scheme (LRCDA) based on distributed architecture. First, the proposed scheme, based on the Chinese Remainder Theorem, enables edge nodes to autonomously establish system keys and independently verify identities, completely eliminating dependence on centralized third parties and mitigating single point of failure risks. Furthermore, the scheme introduces a cross-domain reusable signature mechanism, allowing signature to be securely reused across multiple security domains during its validity period, thereby reducing the frequency of signature generation and minimizing authentication latency. Moreover, through efficient task allocation, computationally intensive operations are offloaded to edge nodes, thereby reducing the computational burden on terminal devices. Theoretical analysis demonstrates that the LRCDA scheme meets the security and privacy requirements for cross-domain authentication in industrial IoT environments, encompassing anonymity, signature unforgeability, and resistance to man-in-the-middle attacks. Experimental evaluations reveal that LRCDA scheme reduces authentication latency by 77.4% compared to traditional schemes and by 88.2% compared to existing distributed approaches, demonstrating significant efficiency gains for resource-constrained industrial IoT deployments.

The Industrial Internet of Things (IIoT) is currently confronted with significant security challenges, including the complexities associated with cross-domain device permission management, the risks of privacy breaches during data transmission, and the vulnerabilities inherent in centralized security architectures to various forms of attack. Existing access control schemes are inadequate for addressing the dynamic and intricate nature of industrial environments, often falling short of meeting the IIoT system’s flexibility, security, and scalability requirements. This article introduces a novel hybrid access control scheme based on blockchain technology within a cross-domain context to tackle these issues. This approach synergizes the simplicity of role-based access control (RBAC) with the adaptability offered by attribute-based access control (ABAC) while harnessing blockchain’s decentralization, immutability, and transparency to bolster system security, autonomy, and capabilities for cross-domain management. We assessed this scheme’s effectiveness in defending against various threats through comprehensive security analysis. Furthermore, experimental results indicate that our proposed solution provides robust security guarantees and surpasses traditional approaches regarding efficiency and performance. Consequently, the scheme presented herein signifies a substantial innovation in IIoT security practices—offering a feasible and reliable solution tailored for complex cross-domain environments.

Mobile crowdsensing (MCS) utilizes sensors distributed across different vehicles to support intelligent transportation and environmental monitoring. Recently, most of the research on MCS in Internet of Vehicles (IoV) mainly focuses on privacy protection and data security of anonymous authentication, but there are still shortcomings in reliability evaluation and reputation management of sensing vehicles. Besides, the lack of effective management of identity information may lead to difficulties in tracking malicious behavior. In this article, we propose a blockchain-assisted anonymous authentication and reputation management (BARM) scheme for MCS in IoV. Specifically, an efficient anonymous authentication algorithm is proposed for sensing vehicles. Then, the privacy protection reputation evaluation algorithm is proposed to ensure the reliability of the sensing vehicles and the security of sensing data. Meanwhile, an accurate reputation update algorithm is proposed to effectively check and update the reputation values of participating sensing vehicles. Besides, the smart contracts are written and deployed on the blockchain to manage the information of the sensing vehicles, which improves the security, efficiency, and trust of the identity management. Subsequently, a formal security verification method based on colored petri net (CPN) and Dolev-Yao attacker model is proposed to evaluate the security of the scheme. The evaluation results show that the scheme can effectively resist a variety of different types of attacks and has multiple security attributes. Performance analysis shows that the proposed scheme has low computation and communication overheads and high robustness.

With the increasing demand for sensing data, the selection efficiency of task participants in mobile crowdsensing (MCS) becomes more and more important. How to efficiently choose eligible task participants while preserving privacy is an urgent issue. In this paper, we propose an efficient and privacy-preserving participant selection scheme based on location in MCS, named EPPS. Specifically, the k-anonymity technique is adopted to generate anonymous location matrices to preserve location privacy of sensing tasks and task participants. To improve selection efficiency, the anonymous location matrices of multiple sensing tasks are consolidated into a matrix called merged location matrix. Similarly, the anonymous location matrices of multiple task participants are also merged. Then, the cloud server computes the Hadamard product for merged location matrices to judge whether task participants locate in the corresponding sensing areas. By judging the merged location matrices of sensing tasks and task participants, multiple participants which satisfy the requirements of sensing tasks can be selected at the same time. Finally, the performance evaluations demonstrate that the proposed scheme outperforms the existing schemes in terms of efficiency, to be specific, the computation overhead of EPPS is decreased by 77% - 85%, the communication overhead of EPPS is decreased by 41%, the selection efficiency of the EPPS is increased by 66% when multiple task participants and one sensing tasks, and the selection efficiency of the EPPS is increased by 96% when multiple task participants and multiple sensing tasks.

In the era of Industry 4.0, the proliferation of location-based services (LBSs) and Industrial Internet of Things (IIoT) asset tracking has revolutionized supply chains and smart manufacturing, offering unprecedented benefits but raising privacy concerns. Continuous asset monitoring and geolocation data can potentially expose confidential information. Existing privacy methods like k-anonymity and l-diversity can preserve the location privacy of IIoT asset tracking devices. However, privacy concerns persist due to inadequate protection against synonymous linkages and probability-distributed attacks. The inherent semantic relevance of sensitive location data may maintain synonymous linkages, and the uneven distribution of historical query probabilities in anonymous spatial regions amplifies the likelihood of adversaries identifying the precise location of IIoT asset tracking devices, resulting in significant privacy breaches. To address these challenges, this article introduces the location diversity-based microaggregation (LDM) method, which enhances privacy protection for IIoT asset tracking by mitigating synonymous linkages and probability-distributed attacks. LDM employs indicators to evaluate synonymous linkages, improving clustering in anonymous microaggregation. It also introduces a caching mechanism to reduce computational costs and prevent unnecessary connections to untrusted LBS providers, reinforcing privacy preservation. Experimental analysis validates the efficiency of the proposed algorithm, demonstrating a reduction in synonymous linkages and superior privacy protection compared to existing state-of-the-art methods. This research represents a crucial step in ensuring robust privacy within IIoT environments and addressing the evolving landscape of LBS in Industry 4.0.

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.

We have witnessed significant technological advancement over the past few years, including the Internet of Things (IoT). The IoT’s ability to connect consumer appliances to the internet has changed the way we live. As a result of the significant benefits that IoT has brought to household usage, it has become a topic of discussion for research departments, leading to its expansion into industrial sectors, commonly known as the Industrial Internet of Things (IIoT). IIoT enables automation and the use of intelligent machines to improve product manufacturing processes and enhance our lives as customers. However, as IIoT-enabling technology and applications continue to grow, security issues and privacy protection challenges become harder to manage, which frequently results in data breaches and sensitive information disclosures. This paper first explains what the reader needs to know about the IIoT system architecture in Industry 4.0 to make it easier for them to understand. Second, a hacking scenario is utilized as a methodology to conduct an in-depth analysis of various security issues, as well as their impacts and countermeasures, for each level of the IIoT architecture. Additionally, our hacking scenarios present a variety of targets from which malicious actors can launch their assaults. Third, we provide a thorough review of the various blockchain solutions currently being employed to protect IIoT systems. Finally, this paper draws to a close by outlining certain gaps and potential solutions that could be investigated in subsequent studies to strengthen security and enhance privacy for IIoT systems.

The traditional Industrial Internet of Things (IIoT) is a centralized system that fully trusts and relies on a central cloud server to process and store the data collected by the equipments. As the number of devices increases, this centralized model not only faces severe network load issues and single point of failure crises, but also raises various security and privacy concerns. Fortunately, blockchain can provide decentral-ization, high trustworthiness and security. Therefore, to design a distributed IIoT system, a blockchain-based IIoT would be a reasonable choice. This paper combines the consortium chain and the homomorphic Paillier algorithm, and proposes a secure distributed data aggregation and verfication (SDAV) mechanism based on the consortium chain. This mechanism combines edge computing and blockchain technology to build a distributed data aggregation framework, which effectively supports the secure collection and storage of data, and avoids single point of failure and tampering crisis. Secondly, based on the (k, t)-threshold Paillier algorithm, a secure cryptographic verification mechanism is designed to protect the privacy and confidentiality of data on the blockchain. Finally, the analysis proves the security of the proposed mechanism and demonstrates the efficiency advantage through simulation experiments.

With the rapid development of information technology, different organizations cooperate with each other to share data information and make full use of data value. Not only should the integrity and privacy of data be guaranteed but also the collaborative computing should be carried out on the basis of data sharing. In this paper, in order to achieve the fairness of data security sharing and collaborative computing, a security data collaborative computing scheme based on blockchain is proposed. A data storage query model based on Bloom filter is designed to improve the efficiency of data query sharing. The MPC contract is designed according to the specific requirements. The participants are rational, and the contract encourages the participants to implement the agreement honestly to achieve fair calculation. A secure multiparty computation based on secret sharing is introduced. The problem of identity and vote privacy in electronic voting is solved. The scheme is analyzed and discussed from storage expansion, anticollusion, verifiability, and privacy.

The emergence of edge computing has improved the real time and efficiency of the Industrial Internet of Things. In order to achieve safe and efficient data collection and application in the Industrial Internet of Things, a lot of computing and bandwidth resources are usually sacrificed. From the perspective of low computing and communication overhead, this paper proposes an efficient privacy protection layered data aggregation scheme for edge computing assisted IIoT by combining the Chinese Remainder Theorem (CRT), improved Paillier homomorphic algorithm, and hash chain technology (edge computing assisted an efficient privacy protection layered data aggregation scheme for IIoT, EE-PPDA). In EE-PPDA, first, a layered aggregation architecture based on edge computing is designed. Edge nodes and cloud are responsible for local aggregation and global aggregation, respectively, which effectively reduces the amount of data transmission. At the same time, EE-PPDA achieves data confidentiality through improved Paillier encryption, ensuring that neither attackers nor semitrusted nodes (e.g., edge nodes and clouds) can know the private data of a single device, and it can resist by simply using hash chains to resist tampering and pollution attacks ensure data integrity. Second, according to the CRT, the cloud can obtain the fine-grained aggregation results of subregions from the global aggregation results, thereby providing fine-grained data services. In addition, the EE-PPDA scheme also supports fault tolerance. Even if some IIoT devices or communication links fail, the cloud can still decrypt incomplete aggregated ciphertexts and obtain the expected aggregation results. Finally, the performance evaluation shows that the proposed EE-PPDA scheme has less calculation and communication costs.

A few of existing multi-path routing protocols also address security issues. In this paper, a concept of plausible-route set is presented. Based on this notion, a definition of security for multiple node-disjoint paths secure source routing protocol is proposed and developed.