In our paper, we design an improved symmetric homomorphic cryptosystem and a fog-based communication architecture to support a delay- or time-sensitive monitoring system. Specifically, the medical data can be analyzed at the fog servers in a secure manner. This will facilitate decision making. For example, relevant stakeholders can detect and respond to emergency situations, based on the analysis of the data. We present two attack games to demonstrate that our approach is secure (i.e. chosen plaintext attack resilience under the computational Diffie-Hellman assumption), and evaluate the complexity of its computations. A comparative summary of its performance and three other related approaches suggests that our approach enables privacy-assured medical data aggregation, and the simulation experiments using Microsoft Azure further demonstrate the utility of our scheme.

With the development of the Internet of Things (IoT) technology, many end-users participate in the smart city through their own intelligent mobile devices (such as personal wearable devices, smartphones.) or sensors. The main challenge of the device sensing layer in the edge computing system of the IoT in the smart city is to select the trusted participants. Because not all the intelligent devices of the IoT are trustworthy, some intelligent devices of the IoT may maliciously damage the network or services and affect the service quality of the system. On this basis, an intelligent device selective recommendation mechanism based on the dynamic black-and-white list was proposed to solve the problem of selecting trusted participants to improve the service quality of the edge computing system of the IoT in the smart city. We introduced the evolutionary game theory to theoretically qualitatively study the validity and stability of the trust management mechanism proposed in this paper. The Lyapunov theory was used to prove the validity and stability of the trust management mechanism. The effectiveness of the trust management mechanism was verified by the actual scenario of the personal health monitoring management system and the air-quality monitoring and analysis system in the smart city environment. Experiments showed that the trust management mechanism proposed in this paper has a significant role in promoting the cooperation of multi intelligent devices in the IoT edge computing system. It more reliably resists the malicious attacks to service providers and is suitable for the large-scale IoT edge computing system in the smart city.

As cloud computing becomes prevalent, more and more data owners are likely to outsource their data to a cloud server. However, to ensure privacy, the data should be encrypted before outsourcing. Symmetric searchable encryption allows users to retrieve keyword over encrypted data without decrypting the data. Many existing schemes that are based on symmetric searchable encryption only support single keyword search, conjunctive keywords search, multiple keywords search, or single phrase search. However, some schemes, i.e., static schemes, only search one phrase in a query request. In this paper, we propose a multi-phrase ranked search over encrypted cloud data, which also supports dynamic update operations, such as adding or deleting files. We used an inverted index to record the locations of keywords and to judge whether the phrase appears. This index can search for keywords efficiently. In order to rank the results and protect the privacy of relevance score, the relevance score evaluation model is used in searching process on client-side. Also, the special construction of the index makes the scheme dynamic. The data owner can update the cloud data at very little cost. Security analyses and extensive experiments were conducted to demonstrate the safety and efficiency of the proposed scheme.

Dynamic searchable symmetric encryption (DSSE) allows efficient searches over encrypted databases and also supports clients in their updating of the data, such as those stored in a remote cloud server. However, recent attacks suggest the risk of leakage during such updates, which consequently impacts on the privacy of the queries. In addition, existing DSSE schemes that support forward privacy generally rely on the honest-but-curious server and support only single-keyword retrieval, which limits the application scenarios. In this paper, we present the design of a verifiable DSSE protocol, which supports efficient conjunctive query with forward privacy. In our scheme, the forward index is constructed by a novel form, i.e., $ t$ -puncturable PRFs, and the authentication tag is designed by symmetric cryptography. During conjunctive queries, we narrow the scope by an inverted index, and then we determine the results of the final query through the forward index. Meanwhile, we can use verification tag to check the correctness and completeness of the result. In addition, we give an extension to support the backward privacy and our experimental evaluations show that our proposed approach achieves better performance on both conjunctive queries and updates than other competing solutions, and possesses efficient verification.

The significant volume, variety, velocity of data received from the many Industrial Internet of Things (IIoT) devices and other systems in a cloud-based or fog-based environment can complicate an organization’s effort in ensuring high quality of experience for data users. For example, how do we efficiently and fairly allocate resource among cloud centers, fog service providers and data users? This is particularly crucial for the IIoT environment, such as those in critical infrastructure sectors such as energy and dams. Therefore in this paper, we propose an optimal resource allocation scheme for a fog-based IIoT environment. Specifically, we introduce fog nodes (or fog service providers) that compete with each other to provide services for the data users using resources from the cloud center. To maximize resource utilization, we model the resource allocation problem as a double-stage Stackelberg game, and propose three algorithms to achieve Nash equilibrium and Stackelberg equilibrium. Then, we evaluate the performance of our proposed scheme with and without having fog service providers, as well as with another competing scheme. The findings demonstrate the importance of fog computing in resource allocation, and the performance of our scheme outperforms that of the other scheme.

Medical imaging is crucial for medical diagnosis, and the sensitive nature of medical images necessitates rigorous security and privacy solutions to be in place. In a cloud-based medical system, medical images should be encrypted prior to being outsourced. However, processing queries over encrypted data without first executing the decryption operation is challenging and impractical at present. In the paper, we propose a secure and efficient scheme to find the exact nearest neighbor over encrypted medical images. Instead of calculating the Euclidean distance, we reject candidates by computing the lower bound of Euclidean distance that is related to the mean and standard deviation of data. Unlike most existing schemes, our scheme can obtain the exact nearest neighbor rather than an approximate result. We then evaluate our proposed approach to demonstrate its utility.