The combination of blockchain and Internet of Things technology has made significant progress in smart agriculture, which provides substantial support for data sharing and data privacy protection. Nevertheless, achieving efficient interactivity and privacy protection of agricultural data remains a crucial issues. To address the above problems, we propose a blockchain-assisted federated learning-driven support vector machine (BAFL-SVM) framework to realize efficient data sharing and privacy protection. The BAFL-SVM is composed of the FedSVM-RiceCare module and the FedPrivChain module. Specifically, in FedSVM-RiceCare, we utilize federated learning and SVM to train the model, improving the accuracy of the experiment. Then, in FedPrivChain, we adopt homomorphic encryption and a secret-sharing scheme to encrypt the local model parameters and upload them. Finally, we conduct a large number of experiments on a real-world dataset of rice pests and diseases, and the experimental results show that our framework not only guarantees the secure sharing of data but also achieves a higher recognition accuracy compared with other schemes.

In the Internet of Things (IoT), a large number of devices are connected using a variety of communication technologies to ensure that they can communicate both physically and over the network. However, devices face the challenge of a single point of failure, a malicious user may forge device identity to gain access and jeopardize system security. In addition, devices collect and transmit sensitive data, and the data can be accessed or stolen by unauthorized user, leading to privacy breaches, which posed a significant risk to both the confidentiality of user information and the protection of device integrity. Therefore, in order to solve the above problems and realize the secure transmission of data, this paper proposed EBIAS, a secure and efficient blockchain-based identity authentication scheme designed for IoT devices. First, EBIAS combined the Elliptic Curve Cryptography (ECC) algorithm and the SHA-256 algorithm to achieve encrypted communication of the sensitive data. Second, EBIAS integrated blockchain to tackle the single point of failure and ensure the integrity of the sensitive data. Finally, we performed security analysis and conducted sufficient experiment. The analysis and experimental results demonstrate that EBIAS has certain improvements on security and performance compared with the previous schemes, which further proves the feasibility and effectiveness of EBIAS.

Large Language Models (LLMs) are complex artificial intelligence systems, which can understand, generate, and translate human languages. By analyzing large amounts of textual data, these models learn language patterns to perform tasks such as writing, conversation, and summarization. Agents built on LLMs (LLM agents) further extend these capabilities, allowing them to process user interactions and perform complex operations in diverse task environments. However, during the processing and generation of massive data, LLMs and LLM agents pose a risk of sensitive information leakage, potentially threatening data privacy. This paper aims to demonstrate data privacy issues associated with LLMs and LLM agents to facilitate a comprehensive understanding. Specifically, we conduct an in-depth survey about privacy threats, encompassing passive privacy leakage and active privacy attacks. Subsequently, we introduce the privacy protection mechanisms employed by LLMs and LLM agents and provide a detailed analysis of their effectiveness. Finally, we explore the privacy protection challenges for LLMs and LLM agents as well as outline potential directions for future developments in this domain.

Federated learning is a powerful technique that enables collaborative learning among different clients. Prototype-based federated learning is a specific approach that improves the performance of local models by integrating class prototypes. However, prototype-based federated learning faces several challenges, such as prototype redundancy and prototype failure, which can limit its accuracy. In addition, it is also susceptible to poisoning attacks and server malfunction, which can degrade the quality of prototypes. To address these issues, we propose FedRFQ, a prototype-based federated learning approach that aims to reduce redundancy, minimize failure, and improve quality. FedRFQ leverages the SoftPool mechanism with prototype-based federated learning, which effectively mitigates prototype redundancy and prototype failure on Non-IID data. Moreover, we introduce the BFT-detect algorithm, a BFT detectable aggregation algorithm, to ensure the security of FedRFQ against poisoning attacks and server malfunction. Finally, we conducted experiments on three different datasets, namely MNIST, FEMNIST, and CIFAR-10. The results demonstrate that FedRFQ outperforms existing baselines in terms of accuracy when handling Non-IID data.

Sharing the big data generated by IoT via cloud is slow and expensive. Besides, transmitting and sharing data among IoT devices via cloud may be insecure. To address these issues, a novel efficient and secure data sharing scheme termed EB-SDSS (Edge Blockchain Secure Data Sharing Scheme) is proposed in this paper for edge-enabled IoT applications. EB-SDSS constructs a blockchain on edge servers. It guarantees the confidentiality and unforgeability of data by combining the symmetric encryption scheme with an edge blockchain. To ensure the device authenticity and the reliability of shared data, EB-SDSS adopts a certificateless signature scheme. It also provides efficient large-scale data searches for IoT devices through a locality-sensitive hashing algorithm. EB-SDSS has been proven to be secure against the adaptive chosen message attacks under the random oracle model. The experimental results indicate that EB-SDSS is feasible for IoT inter-device data sharing.

In the current hypergrowth phase of the Internet of Things, cross-domain data access becomes more and more frequently. Whereas, the lack of trust between domains makes cross-domain data access extremely hard. Traditional schemes typically depend on a third party to establish trust between data accessing entities, which can easily result in single point of failure. To conquer the aforementioned challenge, this paper proposes BSCDA, a blockchain-based cross-domain data access scheme designed to enable secure data transmission across domains. The decentralization, transparency, and anti-tampering features of blockchain perfectly solve the issue of single point of failure and foster trust among various domains. Specifically, a certificate management method is developed to address the certificate storage issue by leveraging a mapping table to store the revocation certificate index on the blockchain. This method not only ensures the verifiability of the certificate but also reduces the storage overhead. Additionally, a four-party key agreement mechanism is designed to guarantee the secure data transmission during the process of cross-domain data access. Security analysis prove the feasibility of our proposed scheme. Extensive experiments demonstrate the superiority of our scheme in cross-domain data access.

As one of the most widely used applications of the Internet of Things (IoT), intelligent transportation system (ITS) is of great significance for urban traffic planning, traffic control, and traffic guidance. However, widespread traffic congestion occurs with the increased number of vehicles. The traffic flow prediction is a good idea for traffic congestion. Therefore, many schemes have been proposed for accurate and real-time traffic flow prediction, but there still exist many issues, including low accuracy, weak adaptability and inferior real-time. Meanwhile, the complex spatial and temporal dependencies in traffic flow are still challenging. To address the above issues, we propose a novel spatial-temporal Chebyshev graph neural network model (ST-ChebNet) for traffic flow prediction to capture the spatial-temporal features, which can ensure accurate traffic flow prediction. Concretely, we first add a fully connected layer to fuse the features of traffic data into a new feature to generate a matrix, and then the long short-term memory (LSTM) model is adopted to learn traffic state changes for capturing the temporal dependencies. Then, we use the Chebyshev graph neural network (ChebNet) to learn the complex topological structures in the traffic network for capturing the spatial dependencies. Eventually, the spatial features and the temporal features are fused to guarantee the traffic flow prediction. The experiments show that ST-ChebNet can make accurate and real-time traffic flow prediction compared with other eight baseline methods on real-world traffic data sets PeMS.

The Internet of Things (IoT) changed our lives with huge amounts of data production. Due to source-limited IoT devices, one of the best ways to process the data is cloud storage. However, a series of security and privacy issues arise, such as illegal data access, data tampering, and privacy leak. Though symmetric encryption can guarantee data confidentiality, it cannot realize fine-grained data sharing and searching. The keyword-based searchable attribute-based encryption (KSABE) can achieve data confidentiality and fine-grained access control. More importantly, it realizes a keyword-based search for data users. However, the heavy decryption computation burden and the management of massive user keys appear when implementing attribute-based encryption schemes to IoT. Therefore, this article proposes a blockchain-aided searchable attribute-based encryption (BC-SABE) with efficient revocation and decryption, where the traditional centralized server is replaced with a decentralized blockchain system being in charge of the threshold parameter generation, key management, and user revocation. All revocation tasks are done by the blockchain and it is on longer necessary for ciphertext reencryption and key update. Moreover, users utilize the coalition blockchain to generate partial tokens. Besides, the cloud server contained in our scheme not only stores the massive encrypted data but also performs search and predecryption for users who only require one exponentiation in the group G to decrypt fully. Security analyses prove that our scheme realizes the security under the chosen plaintext attack and the chosen keyword attack. Simulations show that the decryption and token generation cost of our scheme are preferable.

One of the best ways to deal with the massive data generated by the Internet of Things (IoT) is storing them in the cloud. However, outsourced storage raises some security and privacy issues, such as data leaking and illegal access. The attribute-based signcryption (ABSC) is one of the most promising approaches which can ensure the confidentiality and authenticity of data simultaneously. Nonetheless, it not only inherits the fine-grained access control but also the heavy computational cost which is intolerable for most resource-limited IoT devices. In this article, we propose lightweight hybrid-policy ABSC (LH-ABSC), a lightweight ABSC scheme which adopts ciphertext-policy encryption (CPABE) and key-policy attribute-based signature (KPABS). Ciphertext-policy attribute-based signature leads the decision making that who can decrypt to the data owners directly. Meanwhile, the signature is related with data owners' attribute set which can be used to testify the authenticity of data. In particular, LH-ABSC has constant signature size and satisfies public verification which is deeply important for IoT devices. Moreover, LH-ABSC outsources most computing overhead to fog nodes, including signature, verify, and decryption. Comprehensive theoretical analyses, such as confidentiality, unforgeability, and verifiability, are provided. Also, the selective chosen ciphertext security, the selective chosen message security, and signers anonymity are achieved.