Abstract With the explosive development of the Internet of Things (IoT), it is convenient and important to collect health data from medical sensors and smart devices and construct medical knowledge graph. The knowledge graph contributes to investigating the connection between patient and disease, especially for epidemic surveillance. However, it is possible to cause the leakage of sensitive health information due to the untrusted data collector or various malicious attackers. In this paper, we attempt to utilise federated learning to construct a special knowledge graph, that is, individual‐symptom relationship diagram with local differential privacy (LDP‐ISRD), for epidemic risk surveillance, which presents the underlying infectious relationship among individuals. At first, we propose a federated learning‐based framework of LDP‐ISRD by utilising individuals' smart devices in IoT. Then, we leverage locations to determine the connection among individuals in terms of physical contact. Next, we propose a randomised algorithm PrivISRD to implement federated learning‐based LDP‐ISRD, which consists of symptom perturbation and aggregation. Finally, extensive experiments evaluate the impact of various parameters and results demonstrate that LDP‐ISRD has good performance.

As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been applied to enhance analysis and plan functions of MAPE so that self-adaptive systems have optimal adaption to changing conditions. However, most of ML-based approaches don’t utilize CAVs’ connectivity to collaboratively generate an optimal learner for MAPE, because of sensor data threatened by gradient leakage attack (GLA). In this article, we first design an intelligent architecture for MAPE-based self-adaptive systems on web 3.0-based CAVs, in which a collaborative machine learner supports the capabilities of managing systems. Then, we observe by practical experiments that importance sampling of sparse vector technique (SVT) approaches cannot defend GLA well. Next, we propose a fine-grained SVT approach to secure the learner in MAPE-based self-adaptive systems that uses layer and gradient sampling to select uniform and important gradients. At last, extensive experiments show that our private learner spends a slight utility cost for MAPE (e.g., \(0.77\%\) decrease in accuracy) defending GLA and outperforms the typical SVT approaches in terms of defense (increased by \(10\) – \(14\%\) attack success rate) and utility (decreased by \(1.29\%\) accuracy loss).

Construction and analysis of functional brain networks (FBNs) with resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method to diagnose functional brain diseases. Nevertheless, the existing methods suffer from several limitations. First, the functional connectivities (FCs) of the FBN are usually measured by the temporal co-activation level between rs-fMRI time series from regions of interest (ROIs). While enjoying simplicity, the existing approach implicitly assumes simultaneous co-activation of all the ROIs, and models only their synchronous dependencies. However, the FCs are not necessarily always synchronous due to the time lag of information flow and cross-time interactions between ROIs. Therefore, it is desirable to model asynchronous FCs. Second, the traditional methods usually construct FBNs at individual level, leading to large variability and degraded diagnosis accuracy when modeling asynchronous FBN. Third, the FBN construction and analysis are conducted in two independent steps without joint alignment for the target diagnosis task. To address the first limitation, this paper proposes an effective sliding-window-based method to model spatiotemporal FCs in Transformer. Regarding the second limitation, we propose to learn common and individual FBNs adaptively with the common FBN as prior knowledge, thus alleviating the variability and enabling the network to focus on the individual disease-specific asynchronous FCs. To address the third limitation, the common and individual asynchronous FBNs are built and analyzed by an integrated network, enabling end-to-end training and improving the flexibility and discriminability. The effectiveness of the proposed method is consistently demonstrated on three data sets for mild cognitive impairment (MCI) diagnosis.

ASCII art detection and recognition is an important branch of current network information processing. However, due to ASCII art's text-based organization and image-based semantic expression, traditional natural language processing (NLP) and image recognition fail to yield ideal results. This paper designs an ASCII art localization and extraction algorithm based on string distance for highly mixed text and ASCII art, aiming to segment clean ASCII art for subsequent recognition. Additionally, an evaluation standard for ASCII art extraction effectiveness is defined. Experimental results show that the proposed algorithm performs well in locating and extracting ASCII art.

As a distributed intelligent paradigm, fuzzy federated learning (FuzzyFL) can reduce the uncertainty and noise of biomedical data and is suited to enhance the accurate detection of adolescent idiopathic scoliosis (AIS). The advanced paradigm requires the hospitals to share the gradient of the fuzzy deep neural network (FDNN) rather than biomedical data. Not only that, the recent research works have been devoted to privacy-preserving FuzzyFL for secure AIS detection that adds differential privacy-based noise to the gradients against membership inference attack, attribute inference attack. However, a novel reconstruction attack called gradient leakage attack (GLA) on inferring biomedical data over the gradient brings the security challenges to FuzzyFL and, thus, has a negative influence on AIS detection. It is natural to ask a fundamental question: Can differentially private FuzzyFL for AIS detection over biomedical data defend GLA? In this article, we construct a privacy-preserving FuzzyFL framework calledPrivateFuzzyFLthat offers a great opportunity to present the systematic evaluation of the private FDNN threatened by the GLAs. In our experiments on a set of chest X-ray images and four FDNNs, we compare more than ten private fuzzy federated optimization algorithms in terms of the defense effect and the utility cost and derive that, first, the existing private FDNNs in FuzzyFL can offer a certain amount of privacy protection for biomedical data against the GLA; and second, the perturbation algorithm with better defense effect usually causes the worse AIS detection of the FDNN.

As an emerging concept, the metaverse incorporates a range of advanced technologies and offers a great opportunity to enhance the experiences of healthcare in clinical practice and human health. However, many cyber security issues often occur in the metaverse healthcare analytics such as DDoS attack, probe attack, and port scanning attack. Fortunately, 6G-enabled intrusion detection can detect anomalous activities with the help of an anomaly detection algorithm for metaverse healthcare analytics. Nevertheless, different from static data, data streams in metaverse healthcare have the intrinsic characteristics of infiniteness, correlation, and distribution change. Traditional static data anomaly detection algorithms do not consider these characteristics, which may result in low accuracy and efficiency. In this paper, aDataStreamAnomalyDetection (DS_AD) approach driven by 6G network is proposed for metaverse healthcare analytics, which incorporates a sliding window and model update into LSHiForest. DS_AD uses a change detection mechanism to optimize the model update. The core design utilizes hash functions to partition data spaces to find anomalies. To validate the feasibility of DS_AD, multiple groups of experiments are designed and executed on SMTP and HTTP datasets. Experimental results show that compared with baselines, our proposal performs favorably for data streams in terms of accuracy and efficiency.

Proactive caching is proving to be an increasingly efficient way to handle massive amounts of data as mobile edge computing becomes more widespread. Utilizing the edge nodes' closer proximity to end users, caching content beforehand at the edge nodes can enable quick replies to end-user queries and lower transmission latency. The fact that edge servers have limited resources makes prediction for cached material particularly crucial. The collaboration between edge nodes and the protection of user data privacy have not been taken into account by the numerous studies recently proposed for predicting content popularity. This work suggests a proactive caching strategy (LT-FLPC) by using federated learning and the lottery hypothesis to address the above issues. The lottery hypothesis is used to address the problem of user privacy and data protection when edge nodes communicate with the central server. Given the existence of collaborative domains between different edge nodes, federated learning is employed to achieve the proactive caching strategy. The experiment results show that the proposed method significantly outperforms other caching algorithms for estimating the popularity of a piece of content, such as Thompson Sampling, in terms of caching efficiency.

Digital-twin-enabled cloud-centric architecture is a promising evolution trend of sixth generation (6G) network, which brings new opportunities and challenges for mobile video streaming-related services requiring the exponentially increasing traffic demands. Device-to-Device (D2D) communication paradigm is an attractive technique to alleviate the problem. However, the previous research work on D2D built on individuals’ random mobility or position snapshot and cannot guarantee the stable communication flow. In this paper, we leverage the cybertwin as a centric controller and take advantages of crowdsourcing technology to attract mobile users to follow the specified path and share their network resources with other users. The design of the specified path is formulated as a problem of user recruitment optimization with cost constraint, which is a NP-Hard problem. Firstly, we investigate a special case of only one mobile user to offer the network resource and present a pseudo-polynomial time algorithm. Secondly, we present a graph-partition-based approach to solve the more complex case of multiple mobile users. Thirdly, we discuss the least expected budget to achieve the maximum utility in an ideal model. Fourthly, we perform extensive experiments to evaluate and compare the performance with the typical ones in simulated digital-twin-enabled 6G networks.

DSLR cameras can achieve multiple zoom levels via shifting lens distances or swapping lens types. However, these techniques are not possible on smart-phone devices due to space constraints. Most smartphone manufacturers adopt a hybrid zoom system: commonly a Wide ( W ) camera at a low zoom level and a Telephoto ( T ) camera at a high zoom level. To simulate zoom levels between W and T , these systems crop and digitally upsample images from W , leading to significant detail loss. In this paper, we propose an efficient system for hybrid zoom super-resolution on mobile devices, which captures a synchronous pair of W and T shots and leverages machine learning models to align and transfer details from T to W. We further develop an adaptive blending method that accounts for depth-of-field mismatches, scene occlusion, flow uncertainty, and alignment errors. To minimize the domain gap, we design a dual-phone camera rig to capture real-world inputs and ground-truths for supervised training. Our method generates a 12-megapixel image in 500ms on a mobile platform and compares favorably against state-of-the-art methods under extensive evaluation on real-world scenarios.

The widespread use of mobile devices propels the development of new-fashioned video applications like 3D (3-Dimensional) stereo video and mobile cloud game via web or App, exerting more pressure on current mobile access network. To address this challenge, we adopt the crowdsourcing paradigm to offer some incentive for guiding the movement of recruited crowdsourcing users and facilitate the optimization of the movement control decision. In this paper, based on a practical 4G (4th-Generation) network throughput measurement study, we formulate the movement control decision as a cost-constrained user recruitment optimization problem. Considering the intractable complexity of this problem, we focus first on a single crowdsourcing user case and propose a pseudo-polynomial time complexity optimal solution. Then, we apply this solution to solve the more general problem of multiple users and propose a graph-partition-based algorithm. Extensive experiments show that our solutions can improve the efficiency of real-time D2D communication for mobile videos.

Motion blur of fast-moving subjects is a longstanding problem in photography and very common on mobile phones due to limited light collection efficiency, particularly in low-light conditions. While we have witnessed great progress in image deblurring in recent years, most methods require significant computational power and have limitations in processing high-resolution photos with severe local motions. To this end, we develop a novel face deblurring system based on the dual camera fusion technique for mobile phones. The system detects subject motion to dynamically enable a reference camera, e.g., ultrawide angle camera commonly available on recent premium phones, and captures an auxiliary photo with faster shutter settings. While the main shot is low noise but blurry (Figure 1(a)), the reference shot is sharp but noisy (Figure 1(b)). We learn ML models to align and fuse these two shots and output a clear photo without motion blur (Figure 1(c)). Our algorithm runs efficiently on Google Pixel 6, which takes 463 ms overhead per shot. Our experiments demonstrate the advantage and robustness of our system against alternative single-image, multi-frame, face-specific, and video deblurring algorithms as well as commercial products. To the best of our knowledge, our work is the first mobile solution for face motion deblurring that works reliably and robustly over thousands of images in diverse motion and lighting conditions.

The dynamic sparse approximate inverse (SPAI) preconditioner has proven to be effective in accelerating the convergence of iterative methods for large linear systems. Recently, accelerating it on graphics processing unit (GPU) has attracted considerable attention due to the fact that the cost of constructing the preconditioner is high. However, the existing parallel dynamic SPAI preconditioning algorithms on GPU are usually ineffective because of the out-of-memory error for large matrices. This motivates us to investigate how to accelerate the construction of dynamic SPAI preconditioners on GPU. In this article, we propose an efficient dynamic SPAI preconditioning algorithm on GPU, called GDSPAI. For our proposed GDSPAI, there are the following novelties: (1) a well-known dynamic SPAI preconditioning algorithm is substantially modified to address the main challenges of parallelization on GPU, (2) a parallel framework of constructing the dynamic SPAI preconditioner on GPU is presented on the basis of the modified dynamic SPAI preconditioning algorithm; and (3) each component of the preconditioner is computed in parallel inside a group of threads. Experimental results show that the proposed GDSPAI is effective for large matrices, and outperforms the popular preconditioning algorithms in three public libraries, as well as a recent parallel static SPAI preconditioning algorithm.

Distribution estimation of physical symptom is a powerful statistical technique to monitor and predict the situation of infectious diseases. With the widespread popularization of smart devices, edge computing is an efficient computing paradigm, which takes advantage of these devices to implement the safe and fast collection and distribution analysis of physical symptoms. However, the sharing of individuals' data, especially of sensitive information (e.g., age, symptom), inevitably raises the great privacy concerns of people due to the possible leakage and the illegal utilization. It is necessary and important to not only implement the highly intelligent epidemic analysis, but also guarantee the privacy of data. In this paper, we focus on locally private distribution estimation of physical symptom with attribute constraints in edge computing. At first, we present an edge-computing-based framework for distribution estimation of physical symptoms, in which each component undertakes the respective task. Then, we propose a naive privacy algorithm, i.e., NAIVEPRIVDISTEST, to directly perturb the values of quasi-identifying attributes and physical symptoms. Finally, extensive experiments demonstrate that NAIVEPRIVDISTEST has the accurate distribution estimation under the privacy constraints.

With the fast development of various computing paradigms, the amount of data is rapidly increasing that brings the huge storage overhead. However, the existing data deduplication techniques do not make full use of similarity detection to improve the storage efficiency and data transmission rate. In this paper, we study the problem of utilising the duplicate and resemblance detection techniques to further compress data. We first present a framework of FIDCS-ERD, a feature-based intelligent deduplication compression system with extreme resemblance detection. We also introduce the main components and the detailed workflow of our compression system. We propose a content-defined chunking algorithm for duplicate detection and a Bloom filter-based resemblance detection algorithm. FIDCS-ERD implements the intelligent file chunking and the fast duplicate and resemblance detection. By extensive experiments over the real datasets, we demonstrate that FIDCS-ERD has better compression effect and more accurate resemblance detection compared to the existing approaches.

Privacy preservation is one of the greatest concerns in big data. As one of extensive applications in big data, privacy preserving data publication (PPDP) has been an important research field. One of the fundamental challenges in PPDP is the trade-off problem between privacy and utility of the single and independent data set. However, recent research has shown that the advanced privacy mechanism, i.e., differential privacy, is vulnerable when multiple data sets are correlated. In this case, the trade-off problem between privacy and utility is evolved into a game problem, in which payoff of each player is dependent on his and his neighbors' privacy parameters. In this paper, we first present the definition of correlated differential privacy to evaluate the real privacy level of a single data set influenced by the other data sets. Then, we construct a game model of multiple players, in which each publishes data set sanitized by differential privacy. Next, we analyze the existence and uniqueness of the pure Nash Equilibrium. We refer to a notion, i.e., the price of anarchy, to evaluate efficiency of the pure Nash Equilibrium. Finally, we show the correctness of our game analysis via simulation experiments.

The world of industrial automation technology is at the outset of a new era of innovation with the hype of Industry 4.0. Global modern industrial system converges the power of machines, computing, analytics, connectivity, cyber-physical systems, Internet of things, automation, cloud system and data exchange. Industry 4.0 is a revolution towards the digital world of digital factories and smart products. Big data is an integration of multi-disciplinary technologies and facilitates customer by bringing incredible services to a click. Internet of things connected the world of machines by adding communication capability in every device to connect to other devices or access the Internet. Big Data inflict a new horizon of opportunities in these systems. In this paper, challenges and opportunities of industrial big data are revealed in the context of Industry 4.0 with a different perspective. The current study helps the researchers to threshold these modern systems of Industry 4.0 in designing big data algorithms and techniques.

With increasing development of location-based services (LBSs), location privacy preservation has been one of the most concerned problems. A common method is to let a user generate dummy trajectories, which ensures the location privacy of a lot of users in a small area. However, due to the high cost of generating dummy trajectories, it is not reasonable for only one user to undertake the cost. In this paper, we study the cost sharing problem to determine which user to generate dummy trajectories and receive the payment from the others. We construct an auction based model, where each LBS user as a bidder, reports his privacy cost and dummy trajectories. We propose a cost sharing mechanism, which incentives users to report their true cost and the effective degree of privacy for all the users. We also demonstrate that our mechanism satisfies both incentive compatibility and budget balance. We evaluate the performance of the proposed mechanism via simulated experiments.

Attracted by the advantages of cloud computing, more and more services and applications are migrated to this new paradigm. As promising as it is, cloud computing also brings new challenges to many research issues, such as service scheduling. Most existing scheduling methods are offline and could not deal with the uncertainties and dynamics during the execution, especially in the dynamic cloud environment. In view of this challenge, in this paper, we propose an uncertainty-aware evolutionary scheduling method for cloud service provisioning. It aims at dealing with uncertainties during execution and updating the scheduling so as to meet the deadline and optimize the execution cost of cloud applications. Our method consists of two phases, baseline scheduling and evolutionary scheduling during execution. In baseline scheduling, we suggest a reverse-auction-based pricing mechanism for service provisioning. In evolutionary scheduling, an uncertain model with three types of uncertainties is considered and four uncertain events are discussed. Accordingly, the evolutionary scheduling policy is presented based on intermediate workflow to get a global optimal schedule, so as to improve the success rate for the execution of the cloud applications. Finally, experiments are designed and performed to demonstrate the effectiveness of our method.

Abstract With software‐defined networking (SDN) becoming the leading technology for large‐scale networks, it is definitely expected that SDN will suffer various types of distributed denial‐of‐service (DDoS) attacks because of its centralized control logic. However, almost all of existing works concentrate on the controller overloading DDoS attacks, while vulnerabilities exposed by data plane of SDN for DDoS attacks are largely ignored. In this paper, we firstly investigate a flow rule flooding DDoS attack. By thoroughly analyzing the flow table size and miss rate, we find that attackers are able to inflict significant performance degradation over the system with limited volume of attack resource. We then prove that it is possible for attackers to maximize the performance degradation and minimize the attack rate at the same time. Besides the flooding DDoS attack, we also study a novel DDoS attack targeting data plane of SDN. By utilizing the entry lifetime management mechanism of flow tables, this attack almost never exhibits an intensive controller access behavior. It flies under the radar by inflicting non‐notable performance impact on the system, while it creates heavy long‐term financial burden on the target application. Finally, we present a potential countermeasure for this stealthy DDoS attack. Through extensive experiments, we conclude that DDoS attacks targeting data plane are possible. Copyright © 2016 John Wiley & Sons, Ltd.