With the continuous development of Internet of Things (IoT), significant value has been generated, but numerous challenges remain. Recommender systems, as an effective tool to optimize IoT services, can significantly enhance user experience. However, the IoT’s demands for low latency and high-computational load make it difficult for traditional recommender systems to adapt. Moreover, traditional approaches often overlook the dynamic nature of user preferences, which are crucial for determining user satisfaction with IoT services. To address these issues, we propose a novel method called MoreGCN, which quantifies the temporal evolution of user preferences and integrates user interest modeling to accurately match similar users. This approach guides the learning of convolutional networks during the recommendation process. Deployed within a distributed computing framework and combined with meta computing, MoreGCN significantly improves computational efficiency and recommendation accuracy. Experimental results demonstrate that, across three benchmark datasets, MoreGCN consistently outperforms several existing state-of-the-art methods in terms of performance.

The pursuit of refined data analysis and the preservation of privacy in Big Data pose significant concerns. Among the paramount paradigms for addressing these challenges, differential privacy stands out as a vital area of research. However, traditional differential privacy tends to be excessively restrictive when it comes to individuals’ control over their own data. It often treats all data as inherently sensitive, whereas in reality, not all information related to individuals is sensitive and requires an identical level of protection. In this paper, we define secret specification-based differential privacy (SSDP), where the term “secret specification” implies enabling users to decide what aspects of their information are sensitive and what are not, prior to data generation or processing. By allowing individuals to independently define their secret specifications, the SSDP achieves personalized privacy protection and facilitates effective data analysis. To enable the targeted application of SSDP, we further present task-specific mechanisms designed for database and graph data scenarios. Finally, we assess the trade-offs between privacy and utility inherent in the proposed mechanisms through comparative experiments conducted on real datasets, demonstrating the utility enhancements offered by SSDP mechanisms in practical applications.

The rapid evolution of the smart grid has made the security and reliability of communication within the power system an urgent and critically important issue. To address this challenge, authentication and key agreement (AKA) protocols have gained significant attention and are regarded as indispensable tools for ensuring the secure operation of the smart grid. However, traditional AKA protocols are plagued by a series of issues, including cumbersome certificate management, delayed certificate revocation, and vulnerability to man-in-the-middle attacks. With the emergence of certificate-less public key cryptography (CL-PKC), the integration of conventional AKA protocols with CL-PKC has emerged as a prominent trend. This paper presents an enhanced certificate-less AKA protocol for smart grids, named ECL-AKA. Firstly, the paper outlines the architecture and security model of this protocol. Subsequently, it presents the complete workflow of the ECL-AKA protocol. Notably, the ECL-AKA protocol introduces a private key verification step before key agreement, allowing for rapid screening of malicious requests at a lower computational cost, thereby enhancing the protocol’s resistance to various types of attacks. In addition, the ECL-AKA’s security is formally established through rigorous theoretical proofs based on the random oracle model in the paper. Finally, comparative experimental analysis demonstrates that the ECL-AKA exhibits lower computational and communication overhead while satisfying essential security attributes.

Currently, power data trading typically relies on Web pages as the conventional mode of mediation. Nevertheless, dishonest trading Web may secretly resell the data sets of grid companies or have no way of knowing what the buyer has done with the power data, thereby compromising the privacy of power user. This article proposes a privacy-preserving supply-demand consistency matching service scheme (PPMSE) to address the problem of whether the power data provided by the seller aligns with the requirements of the buyer in power data trading. The scheme utilizes enhanced public-key searchable encryption (PKSE) to establish a matching environment that fulfills privacy protection needs, thereby facilitating consistency matching between supply and demand, all while preserving user privacy. Then, the PPMSE ensures that matching service can only occur within the designated platform by equipping the power data trading cloud platform with public and private keys. Additionally, by applying ciphertext policy attribute-based encryption (CP-ABE) to the data processing tasks of the buyer, the scheme enables the seller to decrypt and obtain what the buyer has done with the data after successful matching and meeting specific attributes. Ultimately, a comprehensive analysis and performance evaluation are provided, validating the feasibility and superiority of the proposed scheme.

Data aggregation plays a crucial role in smart grid communication as it enables the collection of data in an energy-efficient manner. However, the widespread deployment of smart meters has raised significant concerns regarding the privacy of users' personal data. Therefore, in this paper, we present an efficient and privacy-preserving data aggregation and trust management scheme (PATM) for an IoT-enabled smart grid based on smart contract. Firstly, we propose a five-layer architecture for smart grid communication to support secure and efficient data aggregation and management. Under the architecture, the Boneh-Goh-Nissim cryptosystem with blind factor is improved to facilitate privacy protection. In addition, the tamper-evident nature of blockchain is utilized for effective data management. Our designs also enhance the resistance to differential attack and prevent privacy breaches during the aggregation process. Detailed security proof and theoretical analysis confirm that our PATM can satisfies the necessary security and privacy requirements while maintaining the required efficiency for smart grid operations. Furthermore, comparative experiments demonstrate that PATM outperforms other proposed work in terms of storage cost, computational complexity, and utility of differential privacy.

With the rapid development of the smart grid (SG), the security and reliability of communication for SG has become an urgent and critical issue. The authentication and key agreement (AKA) protocol has emerged as important means to address the security challenges in smart grid. This paper proposes a novel certificateless authentication and key agreement protocol (NCL-AKA) for the smart grid, which overcomes the limitations of existing AKA protocol such as complex certificate management, untimely certificate revocation, and man-in-the-middle attacks. Firstly, the system architecture and threat model of the proposed protocol is presented. Then, the correctness and security of the proposed protocol are analyzed. Finally, comparative experiments and analysis reveal that the NCL-AKA has lower computational cost and communication overhead.

Internet of Medical Things (IoMT) technologies significantly improve the quality of health care, especially at the time when COVID-19 is becoming a worldwide pandemic. Due to the complexity of devices and user nodes in the IoMT system, there should be some ways to ensure the security and quality of the service or information. Decentralized trust management techniques are efficient means of promoting application security and reliability in these cases. However, the majority of currently utilized access control schemes cannot be applied in decentralized trust management systems or perform poorly owing to the numerous restrictions of decentralized systems. In this article, we present a policy-hiding and multiauthority key generation CP-ABE scheme (PM-CPABE) for decentralized trust management systems, which could provide fine-grained access control capabilities. Meanwhile, the proposed scheme does not require any fully trusted entity, thus it can be well adapted to decentralized trust management systems. The scheme also implements policy hiding to protect user privacy. In addition, it supports large universe and outsourced decryption. The security analyses and performance comparisons give evidence of our scheme is secure and efficient.

With the widespread application of real-time embedded systems (ESs), the contradiction between the energy consumption requirements of modern processors and the limited battery capacity becomes more obvious. Dynamic voltage scaling (DVS) has been proven to be one of the most effective technologies for energy management. However, recent studies have shown that the use of DVS leads to a significant increase in the transient fault rate of processors as the characteristic size of logic gates (or transistors) gets smaller and smaller. In this article, we consider the problem of assigning processing frequencies to a group of periodic real-time tasks so as to minimize the overall energy consumption under the constraints of time and reliability. First, under the DVS, we take the reliability of the ESs into consideration through the regularization terms and present the energy consumption optimization model based on the metaheuristic algorithms. Second, a novel algorithm for adaptive differential whale swarm optimization (ADWOA) is proposed according to the optimization requirements. Finally, the optimized data are saved on the chain through the storable feature of the blockchain for the necessary queries. It is worth noting that the on-chain data contains the intrinsic characteristics of the ES, which may give rise to the disclosure of processor privacy. Therefore, we come up with the differential privacy on-chain creating algorithm (DPCA) to protect the privacy of data on the chain. Experimental results show that ADWOA can minimize the energy consumption in real-time ES on the premise of ensuring system reliability and privacy.

As one of the key technologies to enable the internet of things (IoT), cloud computing plays a significant role in providing huge computing and storage facilities for large-scale data. Though cloud computing brings great advantages, new issues emerge, such as data security breach and privacy disclosure. In this paper, we introduce a novel secure and privacy-preserving outsourcing computing scheme (hereafter referred to as SPOCS) to tackle this issue. In SPOCS, the effective use of Intel SGX, one of the trusted execution environment (TEE), ensures the confidence and integrity of sensitive data in cloud computing and prevents data loss from causing privacy disclosure. In order to keep malicious cloud service providers (CSPs) from illegally tampering with the outsourcing results, blockchain is employed to ensure the data immutability. Significantly, our proposed scheme achieves anonymity and traceability. In the outsourcing process, smart contracts are applied to make the whole process fully automated without any human involvement. Finally, the security of the proposed scheme is analyzed in terms of its resistance to different attacks. The experiments indicate that our scheme is effective and efficient.

Currently, the conventional mode of power data transaction is mediated by Web pages. Nevertheless, there are challenging issues such as privacy protection, transaction security and data reliability in power data trading. In this paper, we present a novel secure power data trading scheme (SPDTS). Firstly, the zero-knowledge proof is employed to achieve data availability and consistency without revealing the data. Then, SPDTS takes full advantage of the dispersibility and immutability of blockchain to ensure the reliability of data transactions. To keep the transaction process efficient, the processing tasks for power data are performed under smart contract. Meanwhile, a trusted execution environment (TEE) is adopted to guarantee the security of power data. Finally, we present a differential privacy scheme to safeguard the privacy information in the power data. Our study indicates that the proposed scheme can achieve privacy protection, transaction security and data reliability. Also, we conduct security analysis and verify the privacy protection property of the scheme in real cases.