In the 5G era, fog-enhanced smart logistics integrates fog computing, along with advanced network communication and information physics systems, into the integrated logistics system to achieve more intelligent and accurate package management and delivery. However, with the mobility of fog nodes, it increases the risk of data privacy leakages and the difficulty of access control management. To address this issue, this article proposes a privacy-preserving scheme with smart contracts and quadtree in 5G for fog-enhanced smart logistics, named PSCQ-FSL. This scheme is based on the logistics chain platform and achieves the generation, circulation, and processing of logistics data by executing preset smart contract algorithms. To protect user privacy, a fine-grained data encryption system has been used here, and precise access control of data is achieved through smart contracts. In addition, the logistics path selection algorithm based on quadtree is designed to ensure the efficiency and fairness of this scheme. Considering the characteristics of the logistics chain, we have improved the delegated proof of stake consensus algorithm to make it more suitable for the proposed scheme. The extended experimental results indicate that the proposed scheme is privacy preserving, secure, and efficient.

The logistics Internet of Things is a new generation logistics model that integrates advanced network communication technology in the traditional logistics industry. It can achieve intelligent and efficient logistics management and personalized services and has attracted the attention of massive researchers and industrial practitioners. However, the leakage of logistics privacy and chaotic access control mechanisms are still noteworthy issues. In this paper, we propose a privacy-preserving scheme with bidirectional option for blockchain-enhanced logistics internet of things, named PB-LIoT. The scheme is based on the blockchain-enhanced logistics IoT architecture, using smart contracts to achieve data access control, ciphertext-policy attribute-based encryption to achieve privacy, and hash function to achieve data integrity detection. To find more efficient delivery routes, we design a logistics routing selection algorithm based on objective optimization. This algorithm considers time efficiency, transportation cost, workload, and other factors, and uses objective optimization to optimize the path. More importantly, we devise a bidirectional choice strategy to achieve more humane services, not only for customers, but also for express delivery sites. Finally, we analyze the security and performance of the scheme. The results show that the proposed scheme in this paper has data privacy protection and efficiency, while considering the humanized factor of bidirectional option.

With the popularization and development of artificial intelligence technology, as well as the increasingly deep integration with various industries, machine learning as a service model is gradually gaining popularity and maturing. However, in the process of model sharing services, there is still data privacy leakage, which poses security risks to data usage security. To address this challenge, this paper proposes a towards incentive with privacy preserving machine learning as a service scheme for crowdsensed data trading. This scheme converts the data sharing problem into a federated learning model sharing problem, and then converts the shared model into an auction model, thereby achieving the transformation of privacy protection issues during the sharing process into privacy auction problems. In auction mode, while ensuring the security of submitted information, characteristics such as utility, individually rational and maximizing social welfare need to be met. Furthermore, in order to ensure fairness and privacy, the bidding information sorting algorithm and pricing strategy under the ciphertext state are designed. Once the winners are determined, the model service sharing mode based on attribute-based encryption and InterPlanetary File System is adopted. The extended experimental results indicate that the proposed scheme meets the characteristics of privacy preserving, flexibility, and efficiency.

Blockchain-enhanced Smart Grid has attracted a lot of attention from scholars in recent years, due to its excellent decentralization, anti-collusion and immutability. With the wide deployment of blockchain and the popularity of more accurate intelligent applications, its role in Smart Grid is becoming more and more irreplaceable. However, because of the threat of quantum computer, it still confronts the risk of privacy disclosure. In this paper, we propose a novel and dynamic range query privacy-preserving scheme for blockchain-enhanced Smart Grid based on lattice. In this scheme, lattice-based homomorphic encryption algorithm is designed to resist the attack of quantum computer and realizes data aggregation to improve efficiency. In particular, this article designs a dynamic range query method with the aid of consortium blockchain by using proxy re-encryption. This method avoids the communication pressure caused by repeated data collection and improves the query efficiency and user experience. In addition, dynamic ciphertext and users update are considered, which further improves the flexibility and feasibility of the scheme. Last but not least, security analysis, exhaustive performance analysis and experiments show that our proposed scheme meets the requirements of dynamic, privacy, security and low computational cost.

With the increasing deployment of Internet of Things (IoT) devices in recent years, blockchain-enhanced IoT has become a new research hotspot. Due to its excellent openness, traceability and other performance, it has immensely promoted the development of this field. However, the traditional “edge-fog-cloud” three-tier architecture model for IoT still faces the threats of privacy disclosure, collusion attack, and low efficiency. To solve these problems, in this article, we creatively design a new consortium blockchain-enhanced IoT architecture, which can enable the permissioned users or participants to enjoy high-quality data services with the assistance of consortium blockchain by taking advantage of its high credibility. Based on this architecture, we propose a novel consortium blockchain-based privacy-preserving range query scheme in the decentralized IoT system, named CBCPRQ. This scheme combines consortium blockchain and inner product function encryption technology to realize flexible, safe, and batch range query of data service in edge computing. Analysis of security shows that our proposed scheme can not only protect the privacy but also perfectly resist the collusion query inference attack and the replay attack. Extensive performance evaluation is implemented by using Hyperledger Fabric platform and demonstrates that our scheme is efficient, scalable, and flexible.

With the advent of the era of Industry 4.0, blockchain-enhanced Smart Grids have received widespread attention from academics and industry personnel, with its great advantages in high transparency, flexibility, and decentralization. However, there are still problems of privacy leakage and inefficiency. In this article, we propose a cross-domain verifiable data collection and privacy-persevering sharing scheme based on lattice in blockchain-enhanced Smart Grids, called VC-DCPS. First, lattice-based homomorphic encryption and spatial–temporal aggregation technology are utilized to enable cross-domain collection of data, dynamic addition and deletion of users and improvement of efficiency. Second, a post-verification method is designed to verify whether the outsourcing cloud executes predetermined operations correctly. Using consensus and incentive mechanisms via smart contract will enable flexible access control and efficient transactions on the blockchain. In addition, formal security analysis shows that the proposed scheme can ensure privacy and enhance security. Finally, performance evaluation and experimental comparison are performed and turn out that the proposed scheme is strong security, high efficiency, and low storage overhead.

The construction of intelligent informatization of the power grid has prompted to accumulate a large amount of text data, and deeply mining the valuable information is significant for the development of the industry. A large number of basic information and information of production process are recorded in the defect text of power equipment. However, repeated expression, unclear logical expression and colloquialism will appear in the process of recording, which makes the operation and maintenance personnel unable to accurately and efficiently manage the logical relationship between the contents of the text. In this paper, we propose a named entity recognition model of BiLSTM-CRF power defect text based on selectable word vector. The model can recognize the category information of professional named entities in the power field from a large number of power defect texts, thereby structuring the massive power defect text data and facilitating the management of text data. In order to further improve the recognition accuracy of the model, we propose an effective optimization scheme. Simulation experiment results show that the proposed model can accurately recognize the information of named entity of defective text in power field. This research can be seen as the foundation for the future defect analysis of power equipment, auxiliary decision support and so on.