As a new computing paradigm, edge computing has appeared in the public field of vision recently. Owning to its advantages of low delay and fast response, edge computing has become an important assistant of cloud computing and has brought new opportunities for diverse smart applications like the smart grid, the smart home, and the smart transportation. However, the accompanying security issues, which have always been the focus of users’ concern, still cannot be ignored. Therefore, we focus on the security issues in this overview. We firstly introduce some related definitions of edge computing and present the architecture for edge computing-based smart applications. After illustrating the smart applications, from the perspective of identity authentication and data security, we analyze the security protection requirements of these smart applications in edge computing environment. Next, we review some state-of-the-art works on them. Furthermore, we present the extended discussions on the applicability of these current works in edge computing environment. Finally, we briefly discuss the future work on authentication and data security of edge computing-based smart applications.

This paper investigates the fundamental covert rate performance in a wireless relaying system consisting of a source-destination pair, a full-duplex (FD) relay and a warden, where the relay can work at either the FD mode or the half-duplex (HD) mode. We first provide theoretical modeling for the instantaneous/average covert rate when the system works solely under the FD mode or HD mode, and then explore the corresponding optimal transmit power control of relay for the covert rate maximization. For an improvement of covert rate, we further propose a joint FD/HD mode that flexibly switches between the FD and HD modes depending on channel state of the relay self-interference channel. Under the joint FD/HD mode, we also examine the related problems of theoretical modeling for covert rate and optimal transmit power control of relay for covert rate maximization. Finally, extensive numerical results are provided to illustrate the covert rate performances of the relaying system under the FD, HD and joint FD/HD modes.

Searchable encryption(SE) allows users to efficiently retrieve data over encrypted cloud data, but most existing SE schemes only support exact keyword search, resulting in false results due to minor typos or format inconsistencies of queried keywords. The fuzzy keyword search can avoid this limitation, but still incurs low search accuracy and efficiency. Besides, most of fuzzy keyword search schemes do not consider malicious cloud servers which may execute a fraction of search operations or forge some results due to various interest incentives such as saving computation or storage resources. To solve these problems, we propose an efficient and Verifiable Ranked Fuzzy Multi-keyword Search scheme, called VRFMS. VRFMS uses locality-sensitive hashing and bloom filter to implement fuzzy keyword search, and employs Term Frequency-Inverse Document Frequency(TF-IDF) to sort the relevant results. Aiming to further improve the search accuracy, we design an improved bi-gram keyword transformation method. Furthermore, the homomorphic MAC technique and a random challenge technique are utilized to verify the correctness and completeness of returned results, respectively. Formal security analysis and empirical experiments demonstrate that VRFMS is secure and efficient in practical applications, respectively.

The development of Industrial Internet of Things (IIoT) provides massive abundant data resources for trading and mining. However, the existing data trading schemes achieve data usage control at the cost of high latency, thereby resulting in poor service quality as the values of IIoT data degrade over time. This article proposes a monitor-based usage control model to enforce data usage policies on the user side, which eliminates frequent interactions between owners and users. Based on that, a data trading scheme with efficient usage control for IIoT (called DTSI) is devised, which utilizes blockchain smart contract and software guard extensions (SGX) to enable owners to fully control users’ identities and operations at minimal overhead. Security analysis shows that DTSI effectively prevents data abuse and ensures the fair exchange of data. Meanwhile, extensive experiments are conducted on the DTSI prototype comparing with the state-of-the-art schemes with real-world IIoT datasets, which demonstrates the efficiency of DTSI.

The impact of fog-assisted Healthcare IoT (H-IoT) system is immense. The smart H-IoT equipments can upload healthcare information to fog nodes with low latency and high mobility. To facilitate secure interactions among three parties, including smart H-IoT equipments, fog nodes and a cloud server, over the public and insecure channels, a few authenticated key agreement (AKA) protocols are proposed. However, existing works are constructed based on expensive cryptographic primitives (e.g., bilinear pairing), which lead to high computation costs. Besides, the anonymity of H-IoT users is failed to be provided. To tackle these issues, an anonymous and lightweight three-party AKA protocol ALAKAP is proposed, which leverages an efficient cryptographic primitive (i.e., Chebyshev chaotic map operation) to generate a shared session key among three parties and achieve security (anonymity and other six properties) and efficiency simultaneously. It then formally proves the security of ALAKAP under the broadly accepted Burrows-Abadi-Needham (BAN) logic model and demonstrates how the proposed protocol satisfies the desired requirements in the fog-assisted H-IoT system. Finally, the performance of ALAKAP is validated by conducting the experiments on Amazon EC2 and Raspberry Pi. The results show that our work can achieve at least 44% higher improvement than the state-of-the-art works.

Image steganalysis is a technique to detect whether an image contains hidden information. Although the existing cross-domain steganalysis methods have been presented to narrow the distribution gap between different domains, it is still challenging to effectively capture the transferable steganalysis representations under the condition of severe distribution shifts. To address this issue, we propose a novel consensus-clustering-based automatic distribution matching scheme, called CADM, which can automatically and accurately match inconsistent distributions in cross-domain steganalysis scenarios. First, the original steganalysis features are clustered by the spatially constrained fuzzy c-means (SCFCM) algorithm with controllable parameters to fully perceive and mine inherent structural relationships. Subsequently, the cluster consensus knowledge is derived from the perspective of intra-domain and inter-domain to facilitate the clustering and the matching. In this way, the representations of weak stego signals can be augmented by identifying cluster centers that can be combined across domains. Ultimately, the cycle-consistent optimization and adaptation is achieved by gradually adjusting the learning strength of well-aligned and poorly-aligned samples to promote the positive transfer of overlapped clusters and prevent the negative transfer of outlier clusters. Furthermore, extensive experiments on various benchmark databases for cross-domain steganalysis demonstrate the superiority of CADM over the current state-of-the-art methods.