IoT-based smart healthcare system (SHS) allows doctors to monitor and diagnose patients remotely, which can greatly ease overcrowding in the hospitals and disequilibrium of medical resources, especially during the rage of COVID-19. However, the SHS generates enormous data which contains sensitive personal information. To protect patients’ privacy, we propose a secure blockchain (BC)-assisted access control scheme for SHS in fog computing. All the operations of users are recorded on the BC by smart contract in order to ensure transparency and reliability of the system. We present a BC-assisted multiauthority attribute-based encryption (MA-ABE) scheme with keyword search to ensure the confidentiality of the data, avoid single point of failure and implement fine-grained access control of the system. IoT devices are limited in resources, therefore it is not practical to apply the BC-assisted MA-ABE scheme directly. To reduce the burdens of IoT devices, We outsource most of the computational tasks to fog nodes. Finally, the security and performance analysis demonstrate that the proposed system is reliable, practical, and efficient.

Recent developments in intelligence technologies have led to an explosion in the use of connected and automated vehicles (CAVs). Unfortunately, these autonomous vehicles face an increasing risk of vulnerability due to various attacks. Vehicle intrusion detection mechanisms are widely employed to mitigate the threats. Although some works have addressed this issue, few works consider situations that the attack detection may be fooled via simultaneous attacking multiple sensors. In this paper, we propose a novel intrusion detection system integrated Space Dimension Model and Time Dimension Model based on sensor data fusion for countering both independent attack and confederate attack in automated vehicles. In Space Dimension Model, the correlations of multivariate in-vehicle sensor data among multiple sensors are utilized as the input of our optimized convolutional neural network (CNN) model. Especially, we construct vehicle state matrices to characterize the underlying correlation of data between each sensor and other sensors, and then input them into our network for classification. To describe the abrupt deviation caused by anomalous multivariate sensor data itself, we design Time Dimension Model to capture the sensor behaviors of vehicle state vectors at adjacent time by utilizing the Mahalanobis distance (MD) metric. Extensive empirical studies based on a real-world vehicular dataset demonstrate the effectiveness of our integrated anomaly detection mechanisms by a comparative analysis with two detection models that consider only Space Dimension and Time Dimension respectively. The method can outperform the related work under different scenarios 1) with the gain of up to 3.01% in accuracy and 3.04% in F1 score; 2) with ability of defending against confederate attack effectively.

To meet the demand for high‐quality healthcare services, data trading can effectively promote the circulation of medical data and improve the level of healthcare services. To address the existing problems of data regulation difficulties and data privacy leakage in medical data trading, a trusted and regulated data trading scheme based on blockchain and zero‐knowledge proof is proposed. In this scheme, a regulatory institution is introduced to control the issuance of authorized tokens and ensure the controllability of data sharing activities. The blockchain takes over the task of generating public parameters to reduce the computational overhead of the system. Based on homomorphic proxy re‐encryption technology, users can perform data analysis in the cloud to ensure data security. Smart contracts and zero‐knowledge proof technology can automatically verify the validity of data to protect the rights and interests of data users; at the same time, efficient consensus algorithms can also increase the rate of transactions processed by the blockchain system. Finally, as the security and performance analysis shows, the scheme in this paper has better security, higher efficiency and more comprehensive functions.

Multicast is an efficient tool to transfer the same data from a publisher to multiple subscribers, and its performance relies critically on the resiliency. Compared with traditional networks, the centralized control and flexible programmability of the Software Defined Network (SDN) is more beneficial for solving the failure recovery problem. This paper presents a design of an OpenFlow controller which can recover and reconstruct a multicast tree rapidly, while reducing the Ternary Content Addressable Memory (TCAM) consumption simultaneously. We choose the protection mechanism, and calculate all the backup paths after the topology information is mastered by the controller. When a failure occurs, affected switches can enable the use of backup paths to continue forwarding multicast packets. As duplicate packets may exist after the failure recovery, the reconstruction of a tree is required by modifying only a small fraction of forwarding rules. The performance evaluations demonstrate that our proposed method recovers a multicast tree with a high speed and low cost. On one hand, the maximum failure recovery time of two protection-based methods including our proposed method is less than 11 ms, while that of two restoration-based methods is 44 ms and 30 ms, respectively. On the other hand, the amount of backup forwarding entries required by another protection-based method exceed our proposed method as long as 3 or more multicast groups exist in the network. Moreover, when our proposed method is adopted, the amount of backup forwarding entries per switch is less than 30, which can be ignored when compared with the capacity of SDN switches (a switch can support a few thousand forwarding rules).

Data outsourcing has become more and more popular due to its low cost and flexibility. However, there is a problem that the cloud server used to store data is partially trusted. Searchable encryption is an efficient technology that is devoted to helping people conduct accurate searches without leaking information. Nonetheless, most existing schemes cannot support dynamic updates or meet the privacy requirements of all users. There have been some experiments to solve these issues by implementing a dynamically searchable asymmetric encryption scheme. This paper proposes an efficient searchable encryption scheme based on the Authenticator Bloom Filter (ABF). The solution can support dynamic updates and multiple users and meet forward and backward security. This paper uses an ABF to improve the efficiency of searches and updates while playing a significant role in dynamic updates. This paper designs a new token encryption scheme and file set encryption scheme, which not only helps users reduce time in searches and updates but also supports multi-user modes. Experiments show that the proposed scheme takes less time in searching and updating algorithms, especially when the keyword does not exist. The solution also takes into account the problem of history storage when updating, which reduces the unnecessary consumption of memory and avoids multiple storage states for the same file.