With the development of the smart traffic, the traditional vehicular Ad hoc Networks (VANETs) and Traffic Estimation and Prediction System (TrEPS) do not satisfy the growing safety requirement, due to the network delay, transmit price and privacy security. In this paper, we propose a group-vehicles oriented reputation assessment scheme for edge VANETs. Firstly, based on edge computing, we build a reputation assessment framework for Group-Vehicles, to validate the correctness of message for other vehicles rapidly. Secondly, through filtering the malicious feedback and faulty message, our scheme can effectively defend against the Bad-mouth attack and Zigzag attack to assure the security of VANETs. Thirdly, the message isolation is implemented by the group-vehicles management, to enhance the privacy security of scheme. In the end, we validate the effectiveness of our scheme through experiments. In other words, even though the proportion of Bad-mouth attack vehicles is about 40%, the precision is 92.12%, and the recall is 88.25%. Also, the proportion of Zigzag attack vehicles is about 40%, the precision is 88.52%, and the recall is 86.75%.

As a novel computing paradigm, fog computing can provide more efficient and high-quality medical services for patients by deploying the smart healthcare system. Smart healthcare needs to store and share patient’s physiological data in fog nodes for online diagnoses. However, if fog nodes of smart healthcare lack effective security mechanisms, user’s privacy data might be stolen by malicious users. Moreover, fog computing needs to confront some brand-new challenges, such as resource-constrained Internet of Things (IoT) and insider attacks. To overcome these challenges, we propose an software-defined networking (SDN)-based security authentication scheme for fog nodes of smart healthcare. In our scheme, an authentication algorithm is deployed in SDN gateway to validate the creditability of fog node, and IoT only need send the privacy and functional attributes to the SDN gateway to decrease the computing of the IoT. To validate the effectiveness and feasibility of our scheme, we implement an experimental system with POX controller and Mininet emulator. The experimental results show that, compared with other similar work, our scheme is more effective, and the computing overhead (${2}{n}{T}_{m}$) and storage overhead [$({n}{+}{2}){\cdot }{1024}{+}{256}$] is lower.

In the fog computing, it is difficult to satisfy the security and privacy requirement for traditional access control system, such as attribute-based access control system. Risk-based access control system can adapt to the dynamic fog environment. However, the existing risk schemes are modeled for users in majority, not for fog nodes, and the context and privacy sensitivity are rarely considered. In this article, we propose a risk assessment scheme for fog nodes in access control system. Firstly, to improve the accuracy of risk score, the risk assessment is modeled with the subject, object, and context for fog nodes; Secondly, we address the risk assessment computing module for every component. Moreover, we depict system model for risk assessment and implement its prototype system risk assessment model. In the end, the reasonability and correctness of computing model are analyzed by proving and simulation. According to the experiments, the accuracy of risk score is higher than that of the work-based access control and dynamic access control for IoT. Therefore, the feasibility and effectiveness of this scheme are proved through the experiments.

To meet user's functional requirements, cloud computing and big data have become the most commonly used computing and data resources. Based on analysis, conversion, extraction and refinement for the big data, a disease can be prevented and group behavior can be predicted. However, each user's private data is also an element in big data. Users must provide private data to the service providers to meet their functional requirements. To gain economic benefits, some SaaS service providers have not been authorized to collect and analyze the user's sensitive private data, as a result, the user's private data is disclosed. In this paper, we propose a private data chain disclosure discovery method, to prevent a user's sensitive privacy information from being illegally disclosed. First, we measure the similarity degree and cost of the disclosure of the private data. Second, according to the similarity degree and cost of disclosure, the disclosure chain and key private data are detected in the process of interaction between user and SaaS service. Third, we propose a discovery framework for the private data chain and demonstrate its feasibility and effectiveness by experiments.

As novel healthcare paradiagm, smart healthcare can provide more efficient and high quality medical services for patients. However, smart healthcare needs patients to share their physiological information for online diagnoses, if the data sharing system of smart healthcare lacks effective security mechanisms, these sensitive information might be abused by illegal or malicious users. Moreover, smart healthcare needs to confront some brand-new challenges, such as resource-constrained IoT things, identity theft attacks and insider attacks. To tackle these problems, we propose a SDN-based security enforcement framework for data sharing systems of smart healthcare. In our framework, each patient has a dedicated virtual machine in data sharing system, each virtual machine provides a group data services which can be released to those authorized service consumers or IoT things. In additon, virtual machine is protected by the SDN-based gateway which provides a firewall mechanism and guarantees only authorized things can access patient's virtual machine. Since each thing has a unique MAC address, thus our framework can effectively authenticate resource-constrained IoT things and tackle the problems caused by identity theft. To validate the effectiveness and feasibility of our framework, we implement an experimental system using POX controller and Mininet emulator. The experimental results illustrate our framework is effective under different test scenarios. As increasing the scale of information flow model, the framework can still work well and its performance can be still acceptable.