Cloud computing is commonly used by both individual and business users due to its powerful computing and storage capabilities. In recent years, identity-based broadcast proxy re-encryption (IBPRE) has been designed to realize more convenient and faster data sharing while guaranteeing the confidentiality of the data. IBPRE is capable of realizing one-to-many data sharing by re-encrypting the initial ciphertext, and then sharing the data with more users. In many practical application scenarios, it needs to satisfy the autonomous conversion of users' decryption privileges, support real-time dynamic management of users, and realize fine-grained access control. However, no existing IBPRE approaches realize the above three functions simultaneously. To this end, dynamic and conditional identity-based broadcast proxy re-encryption with autonomous paths (DC-IBPRE-AP) is proposed. We present the system model, formal definition, and security model of DC-IBPRE-AP, as well as a concrete DC-IBPRE-AP scheme. Compared to the existing IBPRE schemes, our scheme not only realizes the autonomous conversion of decryption privileges among the set of broadcast receivers, but also supports the dynamic user management and fine-grained access control of users. The proposed scheme achieves chosen ciphertext attack security. The performance analysis shows that the new scheme also has significant advantages.

With the wide application of cloud and fog computing, fog devices deployed in scenarios such as the Internet of Vehicles (IoV) are faced with the need to share massive amounts of data, which makes it crucial to implement fine-grained data access control. However, data transmission between fog devices and vehicles in the IoV is highly susceptible to eavesdropping, tampering, and other attacks by malicious vehicles. Most of the existing attribute-based encryption schemes rely on periodic key updates to enable malicious user tracking and attribute revocation, but they cannot meet the demand for real-time security response in the IoV. For this reason, we propose traceable and revocable multi-authority attribute-based encryption with cloud and fog computing (TR-MAABE-CFC). We elaborate on the system model, rigorously define the concepts, and build the security model for TR-MAABE-CFC. Subsequently, we construct a specific implementation scheme for TR-MAABE CFC. Our proposed solution provides fine-grained access control mechanisms, enables the identification of malicious entities, and supports precise attribute revocation. It can be effectively put into practice in cloud and fog computing scenarios. Experimental findings show that our scheme demonstrates superior decryption efficiency and is highly compatible with intelligent transportation system scenarios, making it a viable solution for the IoV.

To achieve the sharing of traffic data with other vehicles, the related data of the Internet of Vehicles (IoV) normally have to be uploaded to the cloud server. Dynamic searchable symmetric encryption (DSSE) empowers users to perform retrieve operations on encrypted databases preserved on cloud servers and supports dynamic data updates at the same time. However, most of the DSSE solutions that support forward privacy and only supply single-keyword search, which places a limit on the practical implementations. When the data owner discloses data to shared users, licensed users might sell their private keys to achieve economic benefits. Based on the aforementioned issues, we put forward traceable and revocable dynamic searchable symmetric encryption with forward privacy and conjunctive query (TR-DSSE-FC), this scheme supports efficient conjunctive-query and the tracking and revocation of malicious users while ensuring forward privacy. This scheme realizes the conjunctive query function by integrating the inverted index with the forward index. The inverted index is deployed to extract the documents that align the least frequent items, while the forward index constructed based on t-puncturable pseudorandom functions (t-Pun-PRFs) is responsible for determining the final query results. To verify whether the search results generated by the cloud server have been modified, we design an accumulative verification label based on symmetric cryptography. Furthermore, we prove the security of TR-DSSE-FC through simulation experiments. According to the performance experiment analysis, compared with analogous approaches, TR-DSSE-FC has significant merits in conjunctive query and verification.

With the evolution of cloud storage, data outsourcing has become a common trend. While cloud storage provides convenient services, its privacy and security issues have also attracted widespread attention. Dynamic searchable symmetric encryption (DSSE) provides an efficacious means of addressing data privacy issues by allowing data owners to directly perform retrieval or update operations on data while it is stored in the cloud. However, traditional DSSE cannot meet the needs of multi-user scenarios in practical applications. In addition, DSSE implements forward privacy and backward that is resistant to file-injection attack and support validation of search results. Therefore, we present verifiable multi-user dynamic searchable symmetric encryption with forward and backward privacy feasible for cloud storage (VM-DSSE-FB). The scheme constructs a DSSE framework that satisfies both forward and backward privacy by fusing symmetric encryption with homomorphic addition and bitmap indexing techniques. Meanwhile, we set up an encrypted update queue for each user to adapt to the requirements of multi-user scenarios. In addition, with the help of bitmap indexing and logicANDoperations, we realize the accurate multi-keyword query function. In particular, the cloud server returns the search results with a workload proof to realize the effective verification of the integrity and correctness of the search results. Security analysis proves that VM-DSSE-FB approach achieves adaptive security. The performance analysis shows that compared with existing similar schemes, VM-DSSE-FB achieves more functionality while improving efficiency and is more suitable for cloud storage services.

Non-probabilistic convex models have significant advantages in dealing with limited sample data with unimodal distributions but often struggle to capture complex features when facing multimodal distribution data effectively. To address this limitation, this paper proposes an adaptive non-probabilistic convex modeling method based on cluster analysis, constructing a new convex polyhedron clustering (CPC) model. The proposed method begins by partitioning sample data into multiple sub-class clusters using a combination of posterior evaluation and K-means clustering techniques. A preprocessing step is incorporated to adaptively adjust the number of sub-class clusters according to the variability within the sample data. Sub-convex polyhedrons are then constructed within each sub-class cluster to quantify localized uncertainty domains. These sub-convex polyhedrons are integrated to form the CPC model, with mathematical expressions derived from the characteristics of convex polyhedrons to model the uncertainty domain of the entire dataset. Additionally, the model introduces safety index and distance index as reliability indices and establishes corresponding criteria to evaluate the performance of the proposed convex modeling method. The effectiveness of the method is further validated through three numerical examples, demonstrating its advantages over existing advanced convex modeling approaches.

Users are inclined to outsource their data to the cloud due to the rapid growth of data in response to the growing use of cloud computing and the Internet of Things (IoT). One of the primary issues for users is the secure sharing of personal data. Access control and fine-grained sharing of encrypted data can be accomplished with attribute-based encryption (ABE). However, the computational cost required for ABE decryption hinders its widespread deployment on lightweight devices. For lightweight mobile devices, outsourcing computation is a highly attractive technical that outsources heavy decryption tasks to the cloud. The majority of current outsourced ABE schemes, however, only verify whether the cloud returns the correct decryption result, and lack a mechanism to prevent mobile devices from maliciously accusing the cloud of returning incorrect results, and overlook fairness between mobile devices and the cloud. Furthermore, the increasing number of encrypted files stored in the cloud will impede efficient query processing. Moreover, attribute revocation and privacy protection are also crucial. Therefore, this paper proposes fair-and-exculpable-attribute-based searchable encryption with revocation and verifiable outsourced decryption (FE-ABSE-RV) Using Smart Contract, and constructs a concrete FE-ABSE-RV scheme. The scheme is capable of withstanding chosen plaintext attack and chosen keyword attack according to security analysis. Theoretical analysis and simulation experiments illustrate that the FE-ABSE-RV scheme is more expressive, efficient, and practical.

With the development of big data, cloud computing is widely used by individual and enterprise users due to its powerful computing and storage capabilities. How to share the encrypted data stored in cloud servers flexibly to a group of users and provide effective access control to the users is a hot topic in current research. For that, this paper proposes fair and verifiable identity-based broadcast proxy re-encryption with designated sender (FV-IBBPRE-DS) for Medical Internet of Things (MIoT), which gives the system model, formal definition, and security model of FV-IBBPRE-DS, and constructs a concrete FV-IBBPRE-DS scheme. The data owner in the FV-IBBPRE-DS scheme is able to designate a sender, which is given the decryption privilege of the encrypted data to a set of receivers. The scheme ensures that the participating entities perform the process of data sharing fairly and are able to verify the consistency of the ciphertexts. The FV-IBBPRE-DS scheme achieves chosen ciphertext attack security. The performance analysis demonstrates that the scheme in this paper has significant advantages in re-encrypted ciphertext generation and decryption phase, and can be applied to resource-constrained MIoT devices.

Data security is becoming increasingly critical as outsourced data services flourish. An effective solution for ensuring data confidentiality in the cloud is attribute-based searchable encryption (ABSE). However, most extant ABSE solutions have not been able to simultaneously achieve rich expressive queries, result validation, attribute revocation, and policy hiding, and there are problems such as inflexibility in query methods and access control capabilities, insufficient scalability and practicality, and privacy leakage. In particular, it should be noted that most ABSE schemes fail to promise the privacy security of access policies and are susceptible to offline keyword guessing attack. Hidden policy-ABSE (HP-ABSE) has been proposed to tackle the issue of explicit attribute values in access policies potentially compromising privacy. Nevertheless, in most existing HP-ABSE schemes, an adversary may initiate attribute values guessing attack to speculate on the values of attribute within access strategy. To this end, this paper proposes partially hidden policy attribute-based multi-keyword searchable encryption with verification and revocation (PHP-ABMKSE-VR). PHP-ABMKSE-VR scheme is practical, secure, as well as effective, making it appropriate for real-world scenarios like smart healthcare, as demonstrated by the experimental outcomes and safety analysis.

In recent years, many extended identity-based broadcast proxy re-encryption (IBPRE) schemes have been put forward. These schemes are flexible enough and feasible to various application scenarios, including conditional IBPRE, revocable IBPRE, and anonymous IBPRE. However, the existing extended IBPRE schemes are not able to simultaneously achieve fine-grained data sharing, identity privacy protection for authorized data users (DUs) and access privilege revocation. To this end, we put forward conditional identity-based broadcast proxy re-encryption with anonymity and revocation (CIBPRE-AR) and construct a concrete CIBPRE-AR scheme. The scheme implements fine-grained data sharing by associating conditions with the re-encryption key. The identity privacy protection for authorized DUs is provided by using Lagrange interpolation. Further, the access privileges of the violated DU are revoked by updating the re-encryption key. The indistinguishability of ciphertexts against chosen-plaintext attack and anonymity of the DUs are proved rigidly. Compared with existing similar schemes, only the CIBPRE-AR scheme simultaneously achieves fine-grained data access control, anonymity as well as revocation. The proposed scheme also has advantage with respect to computation cost.

Abstract Vehicle transportation of hazardous chemicals is one of the important mobile hazards in modern logistics, and its unsafe factors bring serious threats to people's lives, property and environmental safety. Although the current object detection algorithm has certain applications in the detection of hazardous chemical vehicles, due to the complexity of the transportation environment, the small size and low resolution of the vehicle target etc., object detection becomes more difficult in the face of a complex background. In order to solve these problems, the authors propose an improved algorithm based on YOLOv5 to enhance the detection accuracy and efficiency of hazardous chemical vehicles. Firstly, in order to better capture the details and semantic information of hazardous chemical vehicles, the algorithm solves the problem of mismatch between the receptive field of the detector and the target object by introducing the receptive field expansion block into the backbone network, so as to improve the ability of the model to capture the detailed information of hazardous chemical vehicles. Secondly, in order to improve the ability of the model to express the characteristics of hazardous chemical vehicles, the authors introduce a separable attention mechanism in the multi‐scale target detection stage, and enhances the prediction ability of the model by combining the object detection head and attention mechanism coherently in the feature layer of scale perception, the spatial location of spatial perception and the output channel of task perception. Experimental results show that the improved model significantly surpasses the baseline model in terms of accuracy and achieves more accurate object detection. At the same time, the model also has a certain improvement in inference speed and achieves faster inference ability.

In recent years, many cloud service providers adopt the pay-per-query model to offer paid search services to the public. The data owner rents the resources of cloud service providers and charges the data user a fee based on the data volume to be queried. While this commercial model offers flexibility, convenience, and cost-effectiveness, it comes with a significant vulnerability to data breaches. Public-key authentication encryption with keyword search (PAEKS) is a technology which is well applied in the pay-per-query model. But there is no PAEKS scheme applicable to this scenario. For this purpose, we present public-key authentication encryption with similar data search for pay-per-query (PAESS) and construct the first idiographic scheme PAESS-I. PAESS-I utilizes Shamir secret sharing and locality sensitive hashing to implement similar data search, has the verifiability of results, adds the charge function to prevent cloud servers and data users from colluding to deny deductions. We propose the second scheme PAESS-II based on PAESS-I, which is a mobile-friendly lightweight PAESS. Our second scheme operates in the pay-per-query model without pairing and exponential operations. PAESS-I satisfies ciphertext indistinguishability and trapdoor indistinguishability, and sacrifices the computational performance in favor of the pay-per-query model. The optimized PAESS-II is resistant to adaptively-chosen-targets attack, and satisfies ciphertext indistinguishability and trapdoor indistinguishability. PAESS-II distinguishes itself from other existing similar schemes by having the same characteristics as PAESS-I, along with the benefits when it comes to the calculation cost.

With the deep integration of information and communication technology and traditional agriculture, plant protection machinery has begun to develop toward intelligence and unmanned, not only is the labor input reduced, but also the production efficiency can be improved. This paper reviews the research status of plant protection machinery and the technical progress of key components of plant protection machinery, such as walking system, transmission system and working system. This research shows that, plant protection machinery is developing towards intelligence and unmanned. This paper concludes with a summary of the main challenges in the design and development of plant protection machinery, and the outlook regarding the primary development directions of plant protection machinery in the near future.

In Internet of Things (IoT) application scenarios, such as intelligent logistics, secure data access control, and sharing based on dynamic searchable symmetric encryption (DSSE) has become a research hotspot in recent years. DSSE is an encryption technology which gratifies the above requirements, while protecting the sensitive data during the operation. The existing DSSE schemes do not achieve data deduplication and conjunctive queries concurrently, and exist problems, such as complex update process, inflexible query method, and privacy disclosure. For this purpose, this article proposes DSSE for data deduplication and conjunctive queries (DSSE-DC), and constructs a concrete DSSE-DC scheme. Our scheme uses symmetric encryption with homomorphic addition and bitmap index to develop a secure and reliable search architecture, and updates through modular addition. Meanwhile, we add a deduplication mechanism to realize data deduplication which avoids the waste of cloud storage resources. Furthermore, we introduce the idea of inner product matching to achieve efficient conjunctive queries. The adaptive security of the DSSE-DC scheme is proved in the random oracle model. While our scheme satisfies forward and backward privacy. In comparison to the existing DSSE schemes for conjunctive queries, our scheme has advantage in update and search performances, which is applicable to IoT applications like intelligent logistics.