In medical field, previous patients’ cases are extremely private as well as intensely valuable to current disease diagnosis. Therefore, how to make full use of precious cases while not leaking out patients’ privacy is a leading and promising work especially in future privacy-preserving intelligent medical period. In this article, we investigate how to securely invoke patients’ records from past case-database while protecting the privacy of both current diagnosed patient and the case-database and construct a privacy-preserving medical record searching scheme based on ElGamal Blind Signature. In our scheme, by blinded the healthy data of the patient and the database of the iDoctor, respectively, the patient can securely make self-helped medical diagnosis by invoking past case-database and securely comparing the blinded abstracts of current data and previous records. Moreover, the patient can obtain target searching information intelligently at the same time he knows whether the abstracts match or not instead of obtaining it after matching. It greatly increases the timeliness of information acquisition and meets high-speed information sharing requirements, especially in 5G era. What's more, our proposed scheme achieves bilateral security, that is, whether the abstracts match or not, both of the privacy of the case-database and the private information of the current patient are well protected. Besides, it resists different levels of violent ergodic attacks by adjusting the number of zeros in a bit string according to different security requirements.

The Internet of Things (IoT) provides various benefits, which makes smart device even closer. With more and more smart devices in IoT, security is not a one‐device affair. Many attacks targeted at traditional computers in IoT environment may also aim at other IoT devices. In this paper, we consider an approach to protect IoT devices from being attacked by local computers. In response to this issue, we propose a novel behavior‐based deep learning framework (BDLF) which is built in cloud platform for detecting malware in IoT environment. In the proposed BDLF, we first construct behavior graphs to provide efficient information of malware behaviors using extracted API calls. We then use a neural network‐Stacked AutoEncoders (SAEs) for extracting high‐level features from behavior graphs. The layers of SAEs are inserted one after another and the last layer is connected to some added classifiers. The architecture of the SAEs is 6,000‐2,000‐500. The experiment results demonstrate that the proposed BDLF can learn the semantics of higher‐level malicious behaviors from behavior graphs and further increase the average detection precision by 1.5%.

The development of the smart medical industry and equipment has made great progress due to the fusion of the IoT, cloud computing, and big data. In smart IoT healthcare, patients can collect vital parameters from various medical sensors attached to them to detect diseases and make initial diagnoses by themselves. With the powerful storage and computing functions of cloud computing, medical sensor devices deployed in a cloud environment can effectively solve the problems that the devices are highly dispersed, heterogeneous, and have limited processing capabilities. As a result, this method can effectively provide customized and scalable smart medical services for patients. However, because these medical resources share computing resources on the cloud platform, changes in equipment workloads will lead to service performance competition among tenants. Therefore, determining how to achieve performance isolation between tenants and guarantee the service-level agreements (SLAs) of the tenants has become the most concerning issue for cloud service providers. In this article, we propose a performance isolation algorithm for multitenant IoT clouds, which can effectively provide performance isolation between tenants. Experiments show that the message processing delay of tenants working within the allocated quota can be reduced by 82%.

Abstract-Multi-access edge computing network (MEC), as one of the key infrastructures of IoT, provides cloud computing capabilities at the edge of radio access network (RAN) by integrating telecommunication and IT services. Integrating blockchain into the MEC network can provide users with secure, private, and traceable edge computing services at the near end, thereby improving IoT security, privacy, and automated use of resources. Due to some characteristics of the MEC network, there are still some challenges to integrate blockchain and edge computing into one system, especially the consensus algorithm of blockchain. The resources of edge computing nodes are limited, and the scale of the network is constantly expanding. Therefore, the blockchain consensus algorithm for the MEC network should occupy as little computing resources as possible, be green, and be permissionless. This paper proposes a permissionless and scalable consensus algorithm "Hedera" for multi-access edge computing network, which has the advantages of permissionless, security, decentralization, scalability, and greenness. The Hedera consensus algorithm is a hybrid blockchain consensus algorithm that combines the permissionless proof-of-capacity algorithm and the permissioned asynchronous Byzantine algorithm. This paper tests the fairness, throughput, scalability, latency, and resource consumption of the algorithm by developing and deploying a prototype system. The experimental results show that the Hedera algorithm proposed in this paper is fair, the throughput reaches 13986.3TPS, and the resource consumption is much lower than the PoW consensus. By analyzing its security and liveness, it can resist Sybil attack, Nothing-at-stake attack, selfish mining attack, message hijacking attack, and has good liveness.

In Intelligent Transportation Systems, images are the main data sources to be analyzed for providing intelligent and precision cognitive services. Therefore, how to protect the privacy of sensitive images in the process of information transmission has become an important research issue, especially in future no non-private data era. In this article, we design the Rearrangement-Arnold Cat Map (R-ACM) to disturb the relationship between adjacent pixels and further propose an efficient Bit-level Image Encryption Algorithm(Bl-IEA) based on R-ACM. Experiments show that the correlation coefficients of two adjacent pixels are 0.0022 in the horizontal direction, -0.0105 in the vertical direction, and -0.0035 in the diagonal direction respectively, which are obviously weaker than that of the original image with high correlations of adjacent pixels. What's more, the NPCR is 0.996120172, and the UACI is 0.334613406, which indicate that Bl-IEA has stronger ability to resist different attacks compared with other solutions. Especially, the lower time complexity and only one round permutation make it particularly suitable to be used in the time-limited intelligent transportation field.