scholarly journals HAC: Hybrid Access Control for Online Social Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Fangfang Shan ◽  
Hui Li ◽  
Fenghua Li ◽  
Yunchuan Guo ◽  
Ben Niu
Keyword(s):  
Access Control ◽  
Mobile Networks ◽  
Online Social Networks ◽  
Rapid Development ◽  
Prototype System ◽  
Proposed Model ◽  
Control Schemes ◽  
Policy Specification ◽  
Hybrid Access ◽  
Network Technologies

The rapid development of communication and network technologies including mobile networks and GPS presents new characteristics of OSNs. These new characteristics pose extra requirements on the access control schemes of OSNs, which cannot be satisfied by relationship-based access control currently. In this paper, we propose a hybrid access control model (HAC) which leverages attributes and relationships to control access to resources. A new policy specification language is developed to define policies considering the relationships and attributes of users. A path checking algorithm is proposed to figure out whether paths between two users can fit in with the hybrid policy. We develop a prototype system and demonstrate the feasibility of the proposed model.

A Novel Spatio-Temporal Access Control Model for Online Social Networks and Visual Verification

2021 ◽  
Vol 11 (2) ◽  
pp. 17-31
Author(s):  
Lanfang Zhang ◽  
Zhiyong Zhang ◽  
Ting Zhao
Keyword(s):  
Social Network ◽  
Access Control ◽  
Online Social Networks ◽  
Online Social Network ◽  
Rapid Development ◽  
Real Life ◽  
Control Model ◽  
Mobile Internet ◽  
Access Control Model ◽  
Spatio Temporal

With the rapid development of mobile internet, a large number of online social networking platforms and tools have been widely applied. As a classic method for protecting the privacy and information security of social users, access control technology is evolving with the spatio-temporal change of social application requirements and scenarios. However, nowadays there is a lack of effective theoretical model of social spatio-temporal access control as a guide. This paper proposed a novel spatio-temporal access control model for online social network (STAC) and its visual verification, combined with the advantages of discretionary access control, using formal language to describe the access control rules based on spatio-temporal, and real-life scenarios for access control policy description, realizes a more fine-grained access control mechanism for social network. By using the access control verification tool ACPT developed by NIST to visually verify the proposed model, the security and effectiveness of the STAC model are proved.

scholarly journals TEP2MP: A text-emotion prediction model oriented to multi-participant text-conversation scenario with hybrid attention enhancement

2022 ◽  
Vol 19 (3) ◽  
pp. 2671-2699
Author(s):  
Huan Rong ◽  
◽  
Tinghuai Ma ◽  
Xinyu Cao ◽  
Xin Yu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Online Social Networks ◽  
Rapid Development ◽  
Attention Mechanism ◽  
Text Representation ◽  
Search Mechanism ◽  
Proposed Model ◽  
The Government ◽  
Prediction Efficiency ◽  
Target Speaker

<abstract> <p>With the rapid development of online social networks, text-communication has become an indispensable part of daily life. Mining the emotion hidden behind the conversation-text is of prime significance and application value when it comes to the government public-opinion supervision, enterprise decision-making, etc. Therefore, in this paper, we propose a text emotion prediction model in a multi-participant text-conversation scenario, which aims to effectively predict the emotion of the text to be posted by target speaker in the future. Specifically, first, an <italic>affective space mapping</italic> is constructed, which represents the original conversation-text as an n-dimensional <italic>affective vector</italic> so as to obtain the text representation on different emotion categories. Second, a similar scene search mechanism is adopted to seek several sub-sequences which contain similar tendency on emotion shift to that of the current conversation scene. Finally, the text emotion prediction model is constructed in a two-layer encoder-decoder structure with the emotion fusion and hybrid attention mechanism introduced at the encoder and decoder side respectively. According to the experimental results, our proposed model can achieve an overall best performance on emotion prediction due to the auxiliary features extracted from similar scenes and the adoption of emotion fusion as well as the hybrid attention mechanism. At the same time, the prediction efficiency can still be controlled at an acceptable level.</p> </abstract>

scholarly journals A Blockchain-Based Medical Data Sharing Mechanism with Attribute-Based Access Control and Privacy Protection

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yingwen Chen ◽  
Linghang Meng ◽  
Huan Zhou ◽  
Guangtao Xue
Keyword(s):  
Access Control ◽  
Data Sharing ◽  
Privacy Protection ◽  
Rapid Development ◽  
Medical Data ◽  
Searchable Encryption ◽  
Prototype System ◽  
Smart Contract ◽  
Medical Institutions ◽  
Attribute Based Access Control

The rapid development of wearable sensors and the 5G network empowers traditional medical treatment with the ability to collect patients’ information remotely for monitoring and diagnosing purposes. Meanwhile, the health-related mobile apps and devices also generate a large amount of medical data, which is critical for promoting disease research and diagnosis. However, medical data is too sensitive to share, which is also a common issue for IoT (Internet of Things) data. The traditional centralized cloud-based medical data sharing schemes have to rely on a single trusted third party. Therefore, the schemes suffer from single-point failure and lack of privacy protection and access control for the data. Blockchain is an emerging technique to provide an approach for managing data in a decentralized manner. Especially, the blockchain-based smart contract technique enables the programmability for participants to access the data. All the interactions are authenticated and recorded by the other participants of the blockchain network, which is tamper resistant. In this paper, we leverage the K-anonymity and searchable encryption techniques and propose a blockchain-based privacy-preserving scheme for medical data sharing among medical institutions and data users. To be specific, the consortium blockchain, Hyperledger Fabric, is adopted to allow data users to search for encrypted medical data records. The smart contract, i.e., the chaincode, implements the attribute-based access control mechanisms to guarantee that the data can only be accessed by the user with proper attributes. The K-anonymity and searchable encryption ensure that the medical data is shared without privacy leaking, i.e., figuring out an individual patient from queries. We implement a prototype system using the chaincode of Hyperledger Fabric. From the functional perspective, security analysis shows that the proposed scheme satisfies security goals and precedes others. From the performance perspective, we conduct experiments by simulating different numbers of medical institutions. The experimental results demonstrate that the scalability and performance of our scheme are practical.

An overview of the 5G mobile network architecture

2018 ◽  
Author(s):  
Phanidra Palagummi ◽  
Vedant Somani ◽  
Krishna M. Sivalingam ◽  
Balaji Venkat
Keyword(s):  
Wireless Network ◽  
Mobile Networks ◽  
Network Architecture ◽  
Mobile Network ◽  
Communication Technologies ◽  
Future Generation ◽  
Network Function Virtualization ◽  
Low Latency ◽  
High Bandwidth ◽  
Network Technologies

Networking connectivity is increasingly based on wireless network technologies, especially in developing nations where the wired network infrastructure is not accessible to a large segment of the population. Wireless data network technologies based on 2G and 3G are quite common globally; 4G-based deployments are on the rise during the past few years. At the same time, the increasing high-bandwidth and low-latency requirements of mobile applications has propelled the Third Generation Partnership Project (3GPP) standards organization to develop standards for the next generation of mobile networks, based on recent advances in wireless communication technologies. This standard is called the Fifth Generation (5G) wireless network standard. This paper presents a high-level overview of the important architectural components, of the advanced communication technologies, of the advanced networking technologies such as Network Function Virtualization and other important aspects that are part of the 5G network standards. The paper also describes some of the common future generation applications that require low-latency and high-bandwidth communications.

scholarly journals Image-Based Automatic Watermeter Reading under Challenging Environments

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 434
Author(s):  
Qingqi Hong ◽  
Yiwei Ding ◽  
Jinpeng Lin ◽  
Meihong Wang ◽  
Qingyang Wei ◽  
...  
Keyword(s):  
Smart Cities ◽  
Rapid Development ◽  
Mobile Network ◽  
Spatial Layout ◽  
Single Image ◽  
Automatic Instrument ◽  
Intelligent Sensors ◽  
Fifth Generation ◽  
Instrument Reading ◽  
Network Technologies

With the rapid development of artificial intelligence and fifth-generation mobile network technologies, automatic instrument reading has become an increasingly important topic for intelligent sensors in smart cities. We propose a full pipeline to automatically read watermeters based on a single image, using deep learning methods to provide new technical support for an intelligent water meter reading. To handle the various challenging environments where watermeters reside, our pipeline disentangled the task into individual subtasks based on the structures of typical watermeters. These subtasks include component localization, orientation alignment, spatial layout guidance reading, and regression-based pointer reading. The devised algorithms for orientation alignment and spatial layout guidance are tailored to improve the robustness of our neural network. We also collect images of watermeters in real scenes and build a dataset for training and evaluation. Experimental results demonstrate the effectiveness of the proposed method even under challenging environments with varying lighting, occlusions, and different orientations. Thanks to the lightweight algorithms adopted in our pipeline, the system can be easily deployed and fully automated.

scholarly journals Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 894-918
Author(s):  
Luís Rosa ◽  
Fábio Silva ◽  
Cesar Analide
Keyword(s):  
Internet Of Things ◽  
Mobile Networks ◽  
Smart Cities ◽  
Human Mobility ◽  
Rapid Development ◽  
Disruptive Technology ◽  
New Paradigm ◽  
Design And Optimization ◽  
High Level

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.

A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition for corporate bankruptcy prediction

2020 ◽  
pp. 1-17
Author(s):  
Dongqi Yang ◽  
Wenyu Zhang ◽  
Xin Wu ◽  
Jose H. Ablanedo-Rosas ◽  
Lingxiao Yang ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Rapid Development ◽  
Optimization Method ◽  
Selection Method ◽  
Bankruptcy Prediction ◽  
Ensemble Model ◽  
Composition Method ◽  
Classifier Selection ◽  
Multi Stage ◽  
Proposed Model

With the rapid development of commercial credit mechanisms, credit funds have become fundamental in promoting the development of manufacturing corporations. However, large-scale, imbalanced credit application information poses a challenge to accurate bankruptcy predictions. A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition is proposed herein by combining the fuzzy clustering-based classifier selection method, the random subspace (RS)-based classifier composition method, and the genetic algorithm (GA)-based classifier compositional optimization method to achieve accuracy in predicting bankruptcy among corporates. To overcome the inherent inflexibility of traditional hard clustering methods, a new fuzzy clustering-based classifier selection method is proposed based on the mini-batch k-means algorithm to obtain the best performing base classifiers for generating classifier compositions. The RS-based classifier composition method was applied to enhance the robustness of candidate classifier compositions by randomly selecting several subspaces in the original feature space. The GA-based classifier compositional optimization method was applied to optimize the parameters of the promising classifier composition through the iterative mechanism of the GA. Finally, six datasets collected from the real world were tested with four evaluation indicators to assess the performance of the proposed model. The experimental results showed that the proposed model outperformed the benchmark models with higher predictive accuracy and efficiency.

scholarly journals Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Junshu Wang ◽  
Guoming Zhang ◽  
Wei Wang ◽  
Ka Zhang ◽  
Yehua Sheng
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Medical Service ◽  
Rapid Development ◽  
Medical Knowledge ◽  
Language Models ◽  
Computing Environment ◽  
Computing Power ◽  
Cloud Computing Environment ◽  
Proposed Model

AbstractWith the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

scholarly journals Analytical Model for Estimating the Impact of Changing the Nominal Power Parameter in LTE

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
A. B. Vallejo-Mora ◽  
M. Toril ◽  
S. Luna-Ramírez ◽  
M. Regueira ◽  
S. Pedraza
Keyword(s):  
Analytical Model ◽  
Mobile Networks ◽  
Radio Resource Management ◽  
Estimation Accuracy ◽  
Model Assessment ◽  
Power Parameter ◽  
Management Procedure ◽  
Proposed Model ◽  
A Cell ◽  
The Impact

UpLink Power Control (ULPC) is a key radio resource management procedure in mobile networks. In this paper, an analytical model for estimating the impact of increasing the nominal power parameter in the ULPC algorithm for the Physical Uplink Shared CHannel (PUSCH) in Long Term Evolution (LTE) is presented. The aim of the model is to predict the effect of changing the nominal power parameter in a cell on the interference and Signal-to-Interference-plus-Noise Ratio (SINR) of that cell and its neighbors from network statistics. Model assessment is carried out by means of a field trial where the nominal power parameter is increased in some cells of a live LTE network. Results show that the proposed model achieves reasonable estimation accuracy, provided uplink traffic does not change significantly.

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