Multi-dimensional encrypted data aggregation enables smart meters to transmit multi-source local electricity consumption data by encrypting and packaging data, ensuing users’ privacy and flexible power resource scheduling. This paper presents a lightweight fault tolerant multi-dimensional encrypted data aggregation scheme in fog computing-assisted smart grids, called FT-MEDA. To ensure transmitted electricity consumption data confidentiality, smart meters package multi-dimensional data by utilizing Chinese Remainder Theorem, and encrypt them under the modified symmetric homomorphic encryption. In addition, a key negotiation mechanism is devised to avoid the dependency on third-party trusted parties for important parameters allocation. Even if some smart meters within the negotiation group are compromised, the privacy of normal smart meters retains protected. In FT-MEDA, fog nodes (FN) deployed in smart grids, execute edge computing on integrity verification and generating aggregated encrypted data reports which will be sent to the control center (CC) for data anslysis. Security analysis indicates that FT-MEDA not only satisfies basic security requirements (e.g., confidentiality, integrity), but also resists collusion attacks and key leakage. Performance evaluation demonstrates that FT-MEDA has lightweight performance advantages in practical deployment of smart grids.

With highly mobile and flexible-configurable, autonomous aerial vehicles (AAVs) are becoming crucial wireless communication infrastructures. To improve the reliability and throughput of data delivery for wireless networks, network coding, as a progressive technology, can be applied in AAV networks. However, network coding incurs a security problem called pollution attacks for AAV networks. Although homomorphic network coding signature can prevent pollution attacks, existing schemes are not suitable for AAV networks due to cumbersome certificate management, the key-escrow issue, or insecurity. In this article, we propose an efficient certificateless homomorphic network coding signature scheme for secure transmission of AAV networks, which can avoid certificate management and the key-escrow issue. Then our scheme is proven to be secure against adaptive chosen identity-and-subspace attacks in the random oracle model, thus our scheme can guarantee data integrity and authenticity to resist pollution attacks. We provide a performance evaluation for the proposed scheme and prior research, and experimental results illustrate the efficiency and feasibility of our scheme for practical application, reducing the verification overhead by 42.918% for a 72-dimensional data vector.

Cloud computing has emerged as a promising mode for storaging vast quantities of big data, which is vulnerable to potential security threats, making it urgent to ensure data confidentiality and integrity auditing. In addition, as a large number of duplicate data files exist in cloud storage, data deduplication is significant to improve storage efficiency. In this article, we propose a blockchain-assisted fine-grained deduplication and integrity auditing scheme for outsourced large-scale data in cloud storage, achieving internal deduplication and cross-user external deduplication for ciphertexts and authentication tags. By constructing sparse summation ciphertext tree, the scheme implements Proofs of Ownership protocol through vector commitment, and guarantees the retrieval of distributed deduplication data blocks by designing the reconstruction matrix and bloom filter. The scheme exploits blockchain and smart contracts to ensure transparent integrity auditing without a third-party auditor (TPA), thereby avoiding malicious auditing biases. Security analysis and performance evaluation demonstrate the feasibility of the scheme for deploying in cloud storage systems.

Vehicular social networks (VSNs) play a crucial role in intelligent transportation systems, offering high-quality data management services that enhance various aspects of daily life. Due to their convenience, VSN systems, equipped with advanced data search and sharing capabilities, are increasingly integrated into modern vehicles. While earlier VSNs focused on securing data communication between users, the transmission of sensitive vehicle and traffic data, like road conditions and vehicle trajectories, has raised privacy concerns and the risk of data leakage, which could harm vehicle owners’ interests. Historically, these systems focused primarily on securing data communication between VSN users. However, the transmission of sensitive vehicle and traffic data, such as road conditions and vehicle trajectory information, has raised concerns about data privacy and the potential risks of data leakage, which could compromise the interests of vehicle owners. To address these challenges, we propose a novel group-grained data search and sharing scheme for VSN systems. Unlike traditional attribute-based encryption methods used in data management, our approach introduces a group-grained model that enables fine-grained control over search rights and data-sharing isolation, ensuring enhanced data privacy. Additionally, to reduce the computational burden on these Internet of Thing (IoT) devices, our scheme ensures constant-sized keyword index generation, data index generation, trapdoor creation, and decryption processes. We evaluate the efficiency of our construction and compare it with similar constructions. The results demonstrate that our construction is well suited for resource-constrained IoT devices in VSN systems.

Federated Learning (FL) enables participants to collaboratively train a global model by sharing their gradients without the need for uploading privacy-sensitive data. Despite certain privacy preservation of FL, local gradients in plaintext may reveal data privacy when gradient-leakage attacks are launched. To further protect local gradients, privacy-preserving FL schemes have been proposed. However, these existing schemes that require a fully trusted central server are vulnerable to a single point of failure and malicious attacks. Although more robust privacy-preserving decentralized FL schemes have recently been proposed on multiple servers, they will fail to aggregate the local gradients with message transmission errors or data packet dropping out due to the instability of the communication network. To address these challenges, we propose a novel privacy-preserving decentralized FL scheme system based on the blockchain and a modified identity-based homomorphic broadcast encryption algorithm. This scheme achieves both privacy protection and error/dropout tolerance. Security analysis shows that the proposed scheme can protect the privacy of the local gradients against both internal and external adversaries, and protect the privacy of the global gradients against external adversaries. Moreover, it ensures the correctness of local gradients' aggregation even when transmission error or data packet dropout happens. Extensive experiments demonstrate that the proposed scheme guarantees model accuracy and achieves performance efficiency.

Massive physical devices are deployed in the Industrial Internet of Things (IoT) to collect ambiance data while heavy storage and communication cost are imposed on these IoT devices. To overcome this constraint, cloud-assisted technologies are introduced to store and manage the collected data. In order to protect data quality and security, encryption is required before uploading data to remote clouds. Consequently, a search function is added to cloud services to find the specific data. However, traditional data searching schemes are constructed in user-oriented systems, where the search function is mainly involved with the relationship between data and users rather than data and devices. As a result, traditional search schemes are not suitable to find special IoT devices. On the other hand, the status of these devices is described by many attributes, e.g., temperature, clean water storage, and machine speed in an early warning system for industrial sewage disposal equipment. Hence, multi-keyword conjunctive queries for partial attributes should be introduced so as to find the target device more accurately and more efficiently. To address these challenges, we propose a new universal device-oriented keyword searchable encryption (Do-KSE) scheme for cloud-assisted IoT in this paper. Furthermore, the functions of a single and conjunctive keyword search are maintained to handle the device search requirement of partial attributes. We conduct extensive experiments to evaluate the proposed scheme. Experimental results show that our scheme has excellent performance because of the lightweight index and query trapdoor.

Smart city, as a new mode, is introduced to improve the level of city management for modern cities. In smart cities, a kernel field is health management for urban residents. Hospital management, as one of the most important components in health management, is concerned. To provide high-quality medical service for sick residents, accurate patient health data analysis is needed. Thus, data collection in patient health monitoring is necessary. To achieve this, massive Internet-of-Things devices are distributed; in general, they are resource-constrained devices. From this, lightweight index generation is needed. Furthermore, with the development of professional technologies in medical science, the hospital manager has to employ many different types of professional doctors. They need the shared patient health data to do a precise diagnosis and present an efficient therapeutic schedule for each patient. However, many secret details are recorded in the patient health data. Thus, data privacy of the shared patient health data should be maintained. In this article, we propose a new traceable patient health data search system for hospital management in smart cities. In this system, the system manager shares the encrypted patient health data to different doctors at the grain of hospital bed. Each doctor accurately finds a patient with a special feature from the patient health monitoring data. To prevent patient health data leakage, the functions of illegal search query blocking and inside malicious user tracing are designed. The performance analysis shows that our system is practical for lightweight data collecting devices.

Cloud storage has been used to reduce the cost and support convenient collaborations for industrial Internet of things (IIoT) data management. When data owners share IIoT data with authorized parties for data interaction, secure cloud data searching and file access control are fundamental security requirements. In this paper, first we discuss a new insider attack to the Cui's multi-key aggregate searchable encryption scheme, where the unauthorized inside users can guess the other users private keys. Then, we propose a novel file-centric multi-key aggregate keyword searchable encryption (Fc-MKA-KSE) system for the IIoT data in the file-centric framework. Specifically, we present two formal security models, namely, the security models of the indistinguishable selective-file chosen keyword attack and the indistinguishable selective-file keyword guessing attack, which can satisfy the security requirements. Our experimental results show that the proposed scheme achieves computational efficiency.