In the past, the security of most public-key encryption or key encapsulation schemes is shown in an ideal model, where private keys, secret keys and random values are assumed to be absolutely secure to adversaries. However, this ideal model is not practical due to side-channel attacks in the sense that adversaries could gain partial information of these secret values involved in decryption operations

Various communication systems have been developed to integrate the haptic channel in digital communication. Future directions of such haptic technologies are moving towards realistic virtual reality applications and human-robot social interaction. With the digitisation of touch, robots equipped with touch sensors and actuators can communicate with humans on a more emotional and intimate level, such

Point cloud video provides 6 degrees of freedom (6DoF) viewing experiences to allow users to freely select the viewing angles of 3D scenes and is expected to be the next-generation video. This paper studies the point cloud video streaming and proposes a fuzzy logic-based point cloud video streaming scheme to solve the inherent technical issues. In particular, a point cloud video is first partitioned

The traditional Internet has encountered a bottleneck in allocating network resources for emerging technology needs. Network virtualization (NV) technology as a future network architecture, the virtual network embedding (VNE) algorithm it supports shows great potential in solving resource allocation problems. Combined with the efficient machine learning (ML) algorithm, a neural network model close

Blockchain-based cryptocurrencies have received extensive attention recently. Massive data has been stored on permission-less blockchains. The analysis of massive blockchain data can bring huge business values. However, the absence of well-processed up-to-date blockchain datasets impedes big data analytics of blockchain data. To fill this gap, we collect and process the up-to-date on-chain data from

Federated learning (FL) is a distributed machine learning approach that can achieve the purpose of collaborative learning from a large amount of data that belong to different parties without sharing the raw data among the data owners. FL can sufficiently utilize the computing capabilities of multiple learning agents to improve the learning efficiency while providing a better privacy solution for the

Analytics firm Cyence estimated Amazon's four-hour cloud computing outage in 2017 “cost S&P 500 companies at least $150 million” and traffic monitoring firm Apica claimed “54 of the top 100 online retailers saw site performance slump by at least 20 percent”. According to Ponemon, 2015 data center outages cost Fortune 1000 companies between $1.25 and $2.5 billion. Despite potential risks, the cloud

Widely deployed smart cameras are generating a large amount of video data and capable of processing frames on devices. Empowered by edge computing, the video data can also be offloaded to edge servers for processing. By leveraging the on-device processing and computation offloading, we propose a federated video analytics system named FedVision to efficiently provision video analytics across devices

Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging. Recently, federated learning is proposed to train a globally shared model by exploiting a massive amount of user-generated data samples on IoT devices while preventing data leakage. However, the device, statistical and model heterogeneities inherent in

A novel data hiding method for covert communication via the QR code image based on adjustments of the shapes of the QR code modules using image processing techniques is proposed. A module block consisting of two module pairs is taken as the unit for message-bit embedding by adjusting the vertical and horizontal internal boundaries in the module pairs. Eliminations of the resulting boundary line zigzaggedness

Determining the qualitative States-of-the-Internet requires an accurate knowledge of queueing delay over an end-to-end path. However, the measurement of queueing delay in a large network is still considered a complex and open problem. Existing schemes that measure queueing delay compensate for this complexity using a high infrastructural support and administrative access to the path under test even

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