•Smart cross-domain data sharing. Patient’s medical records are encrypted using a cross-domain access policy, which can be access by the authorized users in the entire system.•Smart self-adaptive ...access control. The access control is self-adaptive to normal and emergency situations. A break-glass access method is designed for the emergency situation.•Smart deduplication. This system supports smart deduplication and provides a new access policies combination method, where no plaintext message is leaked.
In this paper, a privacy-preserving smart IoT-based healthcare big data storage system with self-adaptive access control is proposed. The aim is to ensure the security of patients’ healthcare data, realize access control for normal and emergency scenarios, and support smart deduplication to save the storage space in big data storage system. The medical files generated by the healthcare IoT network are encrypted and transferred to the storage system, which can be securely shared among the healthcare staff from different medical domains leveraging a cross-domain access control policy. The traditional access control technology allows the authorized data users to decrypt patient’s sensitive medical data, but also hampers the first-aid treatment when the patient’s life is threatened because the on-site first-aid personnel are not permitted to get patient’s historical medical data. To deal with this dilemma, we propose a secure system to devise a novel two-fold access control mechanism, which is self-adaptive for both normal and emergency situations. In normal application, the healthcare staff with proper attribute secret keys can have the data access privilege; in emergency application, patient’s historical medical data can be recovered using a password-based break-glass access mechanism. To save the storage overhead in the big data storage system, a secure deduplication method is designed to eliminate the duplicate medical files with identical data, which may be encrypted with different access policies. A highlight of this smart secure deduplication method is that the remaining medical file after the deduplication can be accessed by all the data users authorized by the different original access policies. This smart healthcare big data storage system is formally proved secure, and extensive comparison and simulations demonstrate its efficiency.
Multimodal representation learning, which aims to narrow the heterogeneity gap among different modalities, plays an indispensable role in the utilization of ubiquitous multimodal data. Due to the ...powerful representation ability with multiple levels of abstraction, deep learning-based multimodal representation learning has attracted much attention in recent years. In this paper, we provided a comprehensive survey on deep multimodal representation learning which has never been concentrated entirely. To facilitate the discussion on how the heterogeneity gap is narrowed, according to the underlying structures in which different modalities are integrated, we category deep multimodal representation learning methods into three frameworks: joint representation, coordinated representation, and encoder-decoder. Additionally, we review some typical models in this area ranging from conventional models to newly developed technologies. This paper highlights on the key issues of newly developed technologies, such as encoder-decoder model, generative adversarial networks, and attention mechanism in a multimodal representation learning perspective, which, to the best of our knowledge, have never been reviewed previously, even though they have become the major focuses of much contemporary research. For each framework or model, we discuss its basic structure, learning objective, application scenes, key issues, advantages, and disadvantages, such that both novel and experienced researchers can benefit from this survey. Finally, we suggest some important directions for future work.
Data fusion is used to integrate features from heterogeneous data sources into a consistent and accurate representation for certain learning tasks. As an effective technique for data fusion, ...unsupervised multimodal feature representation aims to learn discriminative features, indicating the improvement of classification and clustering performance of learning algorithms. However, it is a challenging issue since varying modality favors different structural learning. In this paper, we propose an efficient feature learning method to represent multimodal images as a sparse multigraph structure embedding problem. First, an effective algorithm is proposed to learn a sparse multigraph construction from multimodal data, where each modality corresponds to one regularized graph structure. Second, incorporating the learned multigraph structure, the feature learning problem for multimodal images is formulated as a form of matrix factorization. An efficient corresponding algorithm is developed to optimize the problem and its convergence is also proved. Finally, the proposed method is compared with several state-of-the-art single-modal and multimodal feature learning techniques in eight publicly available face image datasets. Comprehensive experimental results demonstrate that the proposed method outperforms the existing ones in terms of clustering performance for all tested datasets.
One of the challenging issues in high-resolution remote sensing images is classifying land-use scenes with high quality and accuracy. An effective feature extractor and classifier can boost ...classification accuracy in scene classification. This letter proposes a deep-learning-based classification method, which combines convolutional neural networks (CNNs) and extreme learning machine (ELM) to improve classification performance. A pretrained CNN is initially used to learn deep and robust features. However, the generalization ability is finite and suboptimal, because the traditional CNN adopts fully connected layers as classifier. We use an ELM classifier with the CNN-learned features instead of the fully connected layers of CNN to obtain excellent results. The effectiveness of the proposed method is tested on the UC-Merced data set that has 2100 remotely sensed land-use-scene images with 21 categories. Experimental results show that the proposed CNN-ELM classification method achieves satisfactory results.
In this paper, we propose a privacy-preserving e-health system, which is a fusion of Internet-of-things (IoT), big data and cloud storage. The medical IoT network monitors patient’s physiological ...data, which are aggregated to electronic health record (EHR). The medical big data that contains a large amount of EHRs are outsourced to cloud platform. In the proposed system, the patient distributes an IoT group key to the medical nodes in an authenticated way without interaction round. The IoT messages are encrypted using the IoT group key and transmitted to the patient, which can be batch authenticated by the patient. The encrypted EHRs are shared among patient and different data users in a fine-grained access control manner. A novel keyword match based policy update mechanism is designed to enable flexible access policy updating without privacy leakage. Extensive comparison and simulation results demonstrate that the algorithms in the proposed system are efficient. Comprehensive analysis is provided to prove its security.
•Anonymous identities are assigned to patients and medical nodes, and real identities can be traced.•Authenticated IoT key distribution.•IoT ciphertext are authenticated by the patient, and a batch authentication method is also provided.•Lightweight fine-grained access control.•Flexible subset keyword match based access policy update.
One of challenging issues for task allocation problem in wireless sensor networks (WSNs) is distributing sensing tasks rationally among sensor nodes to reduce overall power consumption and ensure ...these tasks finished before deadlines. In this paper, we propose a soft real-time fault-tolerant task allocation algorithm (FTAOA) for WSNs in using primary/backup (P/B) technique to support fault tolerance mechanism. In the proposed algorithm, the construction process of discrete particle swarm optimization (DPSO) is achieved through adopting a binary matrix encoding form, minimizing tasks execution time, saving node energy cost, balancing network load, and defining a fitness function for improving scheduling effectiveness and system reliability. Furthermore, FTAOA employs passive backup copies overlapping technology and is capable to determinate the mode of backup copies adaptively through scheduling primary copies as early as possible and backup copies as late as possible. To improve resource utilization, we allocate tasks to the nodes with high performance in terms of load, energy consumption, and failure ratio. Analysis and simulation results show the feasibility and effectiveness of FTAOA. FTAOA can strike a good balance between local solution and global exploration and achieve a satisfactory result within a short period of time.
Neural networks have been proved efficient in improving many machine learning tasks such as convolutional neural networks and recurrent neural networks for computer vision and natural language ...processing, respectively. However, the inputs of these deep learning paradigms all belong to the type of Euclidean structure, e.g., images or texts. It is difficult to directly apply these neural networks to graph-based applications such as node classification since graph is a typical non-Euclidean structure in machine learning domain. Graph neural networks are designed to deal with the particular graph-based input and have received great developments because of more and more research attention. In this paper, we provide a comprehensive review about applying graph neural networks to the node classification task. First, the state-of-the-art methods are discussed and divided into three main categories: convolutional mechanism, attention mechanism and autoencoder mechanism. Afterward, extensive comparative experiments are conducted on several benchmark datasets, including citation networks and co-author networks, to compare the performance of different methods with diverse evaluation metrics. Finally, several suggestions are provided for future research based on the experimental results.
Community structure is an important characteristic of complex networks. Uncovering communities in complex networks is currently a hot research topic in the field of network analysis. Local community ...detection algorithms based on seed-extension are widely used for addressing this problem because they excel in efficiency and effectiveness. Compared with global community detection methods, local methods can uncover communities without the integral structural information of complex networks. However, they still have quality and stability deficiencies in overlapping community detection. For this reason, a local community detection algorithm based on internal force between nodes is proposed. First, local degree central nodes and Jaccard coefficient are used to detect core members of communities as seeds in the network, thus guaranteeing that the selected seeds are central nodes of communities. Second, the node with maximum degree among seeds is pre-extended by the fitness function every time. Finally, the top
k
nodes with the best performance in pre-extension process are extended by the fitness function with internal force between nodes to obtain high-quality communities in the network. Experimental results on both real and artificial networks show that the proposed algorithm can uncover communities more accurately than all the comparison algorithms.
Feature representation is generally applied to reducing the dimensions of high-dimensional data to accelerate the process of data handling and enhance the performance of pattern recognition. However, ...the dimensionality of data nowadays appears to be a rapidly increasing trend. Existing unsupervised feature representation methods are susceptible to the rapidly increasing dimensionality of data, which may result in learning a meaningless feature that in turn affect their performance in other applications. In this paper, an unsupervised adversarial auto-encoder network is studied. This network is a probability model that combines generative adversarial networks and variational auto-encoder to perform variational inference and aims to generate reconstructed data similar to original data as much as possible. Due to its adversarial training, this model is relatively robust in feature learning compared with other methods. First, the architecture and training strategy of adversarial auto-encoder are presented. We attempt to learn a discriminative feature representation for high-dimensional image data via adversarial auto-encoder and take its advantage into image clustering, which has become a difficult computer vision task recently. Then amounts of comparative experiments are carried out. The comparison contains eight feature representation methods and two recently proposed deep clustering methods performed on eight different publicly available image data sets. Finally, to evaluate their performance, we utilize a
K
-means clustering on the low-dimensional feature learned from each feature representation algorithm, and select three evaluation metrics including clustering accuracy, adjusted rand index and normalized mutual information, to provide a comparison. Comprehensive experiments prove the usefulness of the learned discriminative feature via adversarial auto-encoder in the tested data sets.
With the development of the Internet of Things, smart devices are widely used. Hardware security is one key issue in the security of the Internet of Things. As the core component of the hardware, the ...integrated circuit must be taken seriously with its security. The pre-silicon detection methods do not require gold chips, are not affected by process noise, and are suitable for the safe detection of a very large-scale integration. Therefore, more and more researchers are paying attention to the pre-silicon detection method. In this study, we propose a machine-learning-based hardware-Trojan detection method at the gate level. First, we put forward new Trojan-net features. After that, we use the scoring mechanism of the eXtreme Gradient Boosting to set up a new effective feature set of 49 out of 56 features. Finally, the hardware-Trojan classifier was trained and detected based on the new feature set by the eXtreme Gradient Boosting algorithm, respectively. The experimental results show that the proposed method can obtain 89.84% average Recall, 86.75% average F-measure, and 99.83% average Accuracy, which is the best detection result among existing machine-learning-based hardware-Trojan detection methods.
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