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Access control mechanisms define the level of access to the resources among specified users. It distinguishes the users as authorized or unauthorized based on appropriate policies. Several traditional and hybrid access control models have been proposed in previous researches over the last few decades. In this study, we provide a detailed survey of access control models and compare the traditional and hybrid access control models based on their access control criteria. This survey focuses on the growing literature of access control models and summarizes it through comparative analysis, identifying limitations and illustrating the advantages of both traditional and hybrid models. This study will help the researchers to get a deep understanding of the traditional and hybrid access control models.

In recent years, ciphertext-policy attribute-based encryption (CP-ABE) has been recognized as a solution to the challenge of the information privacy and data confidentiality in cloud-assisted Internet-of-Things (IoT). Since the devices in cloud-assisted IoT are generally resource-constrained, the lightweight CP-ABE is more suitable for the cloud-assisted IoT. So how to construct the lightweight CP-ABE for the cloud-assisted IoT to achieve the fine-grained access control and ensure the privacy and confidentiality simultaneously is a prominent challenge. Thus, in this paper, we propose a constant-size CP-ABE scheme with outsourced decryption for the cloud-assisted IoT. In our scheme, the ciphertexts and the attribute-based private keys for users are both of constant size, which can alleviate the transmission overhead and reduce the occupied storage space. Our outsourced decryption algorithm is privacy-protective, which means the proxy server cannot know anything about the access policy of the ciphertext and the attributes set of the user during performing the online partial decryption algorithm. This will prevent the privacy from leaking out to the proxy server. And we rigorously prove that our scheme is selectively indistinguishably secure under the chosen ciphertext attacks (IND-CCA) in the random oracle model (ROM). Finally, by evaluating and implementing our scheme as well as other CP-ABE schemes, we can observe that our scheme is more suitable and applicable for cloud-assisted IoT.

With the emergence of deep learning, computer vision has witnessed extensive advancement and has seen immense applications in multiple domains. Specifically, image captioning has become an attractive focal direction for most machine learning experts, which includes the prerequisite of object identification, location, and semantic understanding. In this paper, semantic segmentation and image captioning are comprehensively investigated based on traditional and state-of-the-art methodologies. In this survey, we deliberate on the use of deep learning techniques on the segmentation analysis of both 2D and 3D images using a fully convolutional network and other high-level hierarchical feature extraction methods. First, each domain’s preliminaries and concept are described, and then semantic segmentation is discussed alongside its relevant features, available datasets, and evaluation criteria. Also, the semantic information capturing of objects and their attributes is presented in relation to their annotation generation. Finally, analysis of the existing methods, their contributions, and relevance are highlighted, informing the importance of these methods and illuminating a possible research continuation for the application of semantic image segmentation and image captioning approaches.

Development of Internet of Vehicles (IoV) has aroused extensive attention in recent years. The IoV requires an efficient communication mode when the application scenarios are complicated. To reduce the verifying time and cut the length of signature, certificateless aggregate signature (CL-AS) is used to achieve improved performance in resource-constrained environments like vehicular ad hoc networks (VANETs), which is able to make it effective in environments constrained by bandwidth and storage. However, in the real application scenarios, messages should be kept untamed, unleashed, and authentic. In addition, most of the proposed schemes tend to be easy to attack by signers or malicious entities which can be called coalition attack. In this paper, we present an improved certificateless-based authentication and aggregate signature scheme, which can properly solve the coalition attack. Moreover, the proposed scheme not only uses pseudonyms in communications to prevent vehicles from revealing their identity but also achieves considerable efficiency compared with state-of-the-art work, certificateless signature (CLS), and CL-AS schemes. Furthermore, it demonstrates that when focused on the existential forgery on adaptive chosen message attack and coalition attack, the proposed schemes can be proved secure. Also, we show that our scheme exceeds existing certification schemes in both computing and communication costs.

The ongoing coronavirus 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a severe ramification on the global healthcare system, principally because of its easy transmission and the extended period of the virus survival on contaminated surfaces. With the advances in computer-aided diagnosis and artificial intelligence, this paper presents the application of deep learning and adversarial network for the automatic identification of COVID-19 pneumonia in computed tomography (CT) scans of the lungs. The complexity and time limitation of the reverse transcription-polymerase chain reaction (RT-PCR) swab test makes it disadvantageous to depend solely on as COVID-19’s central diagnostic mechanism. Since CT imaging systems are of low cost and widely available, we demonstrate that the drawback of the RT-PCR can be alleviated with a faster, automated, and reduced contact diagnostic process via the use of a neural network model for the classification of infected and noninfected CT scans. In our proposed model, we explore the benefit of transfer learning as a means of resolving the problem of inadequate dataset and the importance of semisupervised generative adversarial network for the extraction of well-mapped features and generation of image data. Our experimental evaluation indicates that the proposed semisupervised model achieves reliable classification, taking advantage of the reflective loss distance between the real data sample space and the generated data.

Content-based (CB) and collaborative filtering (CF) recommendation algorithms are widely used in modern e-commerce recommender systems (RSs) to improve user experience of personalized services. Item content features and user-item rating data are primarily used to train the recommendation model. However, sparse data would lead such systems unreliable. To solve the data sparsity problem, we consider that more latent information would be imported to catch users’ potential preferences. Therefore, hybrid features which include all kinds of item features are used to excavate users’ interests. In particular, we find that the image visual features can catch more potential preferences of users. In this paper, we leverage the combination of user-item rating data and item hybrid features to propose a novel CB recommendation model, which is suitable for rating-based recommender scenarios. The experimental results show that the proposed model has better recommendation performance in sparse data scenarios than conventional approaches. Besides, training offline and recommendation online make the model has higher efficiency on large datasets.

We propose a novel classification framework to precisely identify individuals with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) from normal controls (NC). The proposed method combines three different features from structural MR images: gray-matter volume, gray-level cooccurrence matrix, and Gabor feature. These features can obtain both the 2D and 3D information of brains, and the experimental results show that a better performance can be achieved through the multifeature fusion. We also analyze the multifeatures combination correlation technologies and improve the SVM-RFE algorithm through the covariance method. The results of comparison experiments on public Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method. Besides, it also indicates that multifeatures combination is better than the single-feature method. The proposed features selection algorithm could effectively extract the optimal features subset in order to improve the classification performance.

Data mining techniques are applied to identify hidden patterns in large amounts of patient data. These patterns can assist physicians in making more accurate diagnosis. For different physical conditions of patients, the same physiological index corresponds to a different symptom association probability for each patient. Data mining technologies based on certain data cannot be directly applied to these patients’ data. Patient data are sensitive data. An adversary with sufficient background information can make use of the patterns mined from uncertain medical data to obtain the sensitive information of patients. In this paper, a new algorithm is presented to determine the top K most frequent itemsets from uncertain medical data and to protect data privacy. Based on traditional algorithms for mining frequent itemsets from uncertain data, our algorithm applies sparse vector algorithm and the Laplace mechanism to ensure differential privacy for the top K most frequent itemsets for uncertain medical data and the expected supports of these frequent itemsets. We prove that our algorithm can guarantee differential privacy in theory. Moreover, we carry out experiments with four real-world scenario datasets and two synthetic datasets. The experimental results demonstrate the performance of our algorithm.

The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information.