DNA computing and table based data accessing in the cloud environment

The COVID-19 pandemic has directly impacted the electric power industry;the energy sector has experienced huge losses in electricity production. These losses have also affected the reliability of communication and employees’ performance, hence destabilizing the electric power system. This article aims at achieving two objectives. First, analyzing the impact of the COVID-19 pandemic on the communication of performance (human error and human factors) and energy management in electricity production. Second, to develop a conceptual framework model to alleviate effects of the pandemic on the power sector and then improve energy management and human performance. This paper involves investigating the influence of the COVID-19 pandemic on the global production of electricity in the first quarters of 2019 and 2020. A conceptual model was developed based on a case study. Additionally, to ensure reliability, a variable, namely COVID-19, was used as a moderator to examine the effects of the independent and dependent variables. The results show that scores for the internet of things (IoT) with awareness and communication (A&C) and workplace environment management were high with Cronbach’s alpha value of 0.87 for the IoT and 0.89 for A&C. These numbers are important indicators of factors that could affect performance and energy management and should not be overlooked by the top management. The results also indicate that the pandemic has had a direct effect on the electricity production sector, and the conceptual framework model revealed that COVID-19, as a moderator, has a direct effect on the variables that significantly affect the improvement of both energy management and employee performance. The case study’s results confirm the poor performance in power plant maintenance and operation, in which human error would increase especially in Iraqi power plants that have not yet adopted any internationally recognized standards for energy management. This paper contributes to the literature studying COVID-19’s impact on the electricity sector in two ways: first, by developing a model to assist the electricity production sector mitigating effects of the COVID-19 pandemic, and second, by providing a detailed investigation into the pandemic’s impact on the electricity sector’s global production. The findings are hoped to assist researchers and research centers in understanding the general and specific framework to manage the pandemic’s effects on electricity production. © 2021 Tech Science Press. All rights reserved.

Implications COVID-19 on performance and energy management in the production electricity

Granting users precise access rights is one of the purposes of access control technologies. With the increasing requirements of fine-grained authorization, too strict or too loose access control models may cause many problems. In this paper, aiming at insufficient authorizations in text databases, we propose a risk-aware topic-based access control (RTBAC) model, which uses topics to represent the content relationships between users and data. The RTBAC model also uses risk technologies to grant users corresponding access rights based on their historical behaviours and their access requests. The RTBAC model is a fine-grained access control model, and the authorization of RTBAC can reach the paragraph level. Experimental results show that RTBAC is an efficient access control model and the access control granularity of the RTBAC model is more than 3 times that of the existing content-based access control models.

/pdf/rtbac-a-risk-aware-topic-based-access-control-model-for-text-1r067jqo.pdf

RTBAC: A Risk-Aware Topic-Based Access Control Model for Text Data with Paragraph-Level Authorization

In the open Internet environment, there is a cross-platform access control problem that when a tenant needs to access the business resources of other collaborative platforms through the cloud service platform, the cloud service platform only supports the tenant to access the business resources within the platform. When tenants need to access business resources through the cloud service platform, the authorization method of the cloud service platform is static and the authorization granularity is coarse-grained, so dynamic fine-grained authorization is not supported. To solve the above problems, based on ABAC, this paper proposes a tenant-centric attribute semantic access control policy model for cloud service platforms. The model and its application framework can automatically evaluate whether it has cloud service platform or cross-platform access control rights according to the change of the tenant’s attributes so as to determine whether it can obtain the corresponding business resources. Through a practical case analysis, we prove that the application of ASACPM and its application framework to the cloud service platform have good flexibility, scalability, and practicability. In addition, we design some experimental scenarios to verify that the performance of ASACPM and its application framework meet our expectations and have good reliability, validity, and rationality.

Tenant-Centric Attribute Semantic Access Control Policy Model for the Cloud Service Platform

A deep learning access control model based on user preferences is proposed to address the issue of personal privacy leakage in social networks.Firstly, social users and social data entities are extracted from the social network and used to construct homogeneous and heterogeneous graphs.Secondly, a graph neural network model is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network.Then, high-order neighbor nodes, hidden neighbor nodes, displayed neighbor nodes, and social data nodes are used to update user nodes to expand the depth and breadth of user preferences.Finally, a multi-layer attention network is used to classify user nodes in the homogeneous graph into two classes: allow access and deny access.The fine-grained access control problem in social networks is transformed into a node classification problem in a graph neural network.The model is validated using a dataset and compared with other methods without losing generality.The model improved accuracy by 2.18% compared to the baseline method GraphSAGE, and improved F1 score by 1.45% compared to the baseline method, verifying the effectiveness of the model. 

pdf/deep-learning-social-network-access-control-model-based-on-3jub33y3me.pdf

Deep Learning Social Network Access Control Model Based on User Preferences

RCBAC: A risk-aware content-based access control model for large-scale text data

Predicting and managing the movement of people in a region during epidemics’ outbreak is an important step in preventing outbreaks. The protection of user privacy during the outbreak has become a matter of public concern in recent years, yet deep learning models based on datasets collected from mobile devices may pose privacy and security issues. Therefore, how to develop an accurate crowd flow prediction while preserving privacy is a significant problem to be solved, and there is a tradeoff between these two objectives. In this paper, we propose a privacy-preserving mobility prediction framework via federated learning (CFPF) to solve this problem without significantly sacrificing the prediction performance. In this framework, we designed a deep and embedding learning approach called “Multi-Factors CNN-LSTM” (MFCL) that can help to explicitly learn from human trajectory data (weather, holidays, temperature, and POI) during epidemics. Furthermore, we improve the existing federated learning framework by introducing a clustering algorithm to classify clients with similar spatio-temporal characteristics into the same cluster, and select servers at the center of the cluster as edge central servers to integrate the optimal model for each cluster and improve the prediction accuracy. To address the privacy concerns, we introduce local differential privacy into the FL framework which can facilitate collaborative learning with uploaded gradients from users instead of sharing users’ raw data. Finally, we conduct extensive experiments on a realistic crowd flow dataset to evaluate the performance of our CFPF and make a comparison with other existing models. The experimental results demonstrate that our solution can not only achieve accurate crowd flow prediction but also provide a strong privacy guarantee at the same time.

/pdf/a-privacy-preserving-crowd-flow-prediction-framework-based-1qjhblk8.pdf

A Privacy-Preserving Crowd Flow Prediction Framework Based on Federated Learning during Epidemics

Insufficient authorization and overauthorization are two main problems to be solved in access control systems. If the authorization is too strict, users might not be able to access data that should be accessible. If the authorization is too lax, users might obtain too many access rights, which may cause considerable risks. Finer-grained access control models are needed to solve these problems. In this paper, aiming at insufficient authorization in text databases, we propose the topic-based access control (TBAC) model and two implementation methods of the model (subject-to-object topic-based access control method PD-TBAC and object-to-subject topic-based access control method FD-TBAC). In the TBAC model, the access control decision for each user against each file is totally content-driven. We use the latent Dirichlet allocation (LDA) algorithm to extract topics from each paragraph in each file, and these topics are used to determine users’ access rights. Experimental results show that the access control granularity of TBAC is more than 4 times that of the existing content-based access control model.

TBAC: A Fine-Grained Topic-Based Access Control Model for Text Data

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RCBAC: A risk-aware content-based access control model for large-scale text data

RTBAC: A Risk-Aware Topic-Based Access Control Model for Text Data with Paragraph-Level Authorization

A Privacy-Preserving Crowd Flow Prediction Framework Based on Federated Learning during Epidemics

TBAC: A Fine-Grained Topic-Based Access Control Model for Text Data