scholarly journals A Cloud Game-Based Educative Platform Architecture: The CyberScratch Project

2021 ◽  
Vol 11 (2) ◽  
pp. 807
Author(s):  
Llanos Tobarra ◽  
Alejandro Utrilla ◽  
Antonio Robles-Gómez ◽  
Rafael Pastor-Vargas ◽  
Roberto Hernández
Keyword(s):  
Data Storage ◽  
Data Privacy ◽  
Data Science ◽  
Critical Issue ◽  
Privacy Management ◽  
Instructional Process ◽  
Legal Context ◽  
Flexible Architecture ◽  
Efficient Data ◽  
Implementation Guidelines

The employment of modern technologies is widespread in our society, so the inclusion of practical activities for education has become essential and useful at the same time. These activities are more noticeable in Engineering, in areas such as cybersecurity, data science, artificial intelligence, etc. Additionally, these activities acquire even more relevance with a distance education methodology, as our case is. The inclusion of these practical activities has clear advantages, such as (1) promoting critical thinking and (2) improving students’ abilities and skills for their professional careers. There are several options, such as the use of remote and virtual laboratories, virtual reality and game-based platforms, among others. This work addresses the development of a new cloud game-based educational platform, which defines a modular and flexible architecture (using light containers). This architecture provides interactive and monitoring services and data storage in a transparent way. The platform uses gamification to integrate the game as part of the instructional process. The CyberScratch project is a particular implementation of this architecture focused on cybersecurity game-based activities. The data privacy management is a critical issue for these kinds of platforms, so the architecture is designed with this feature integrated in the platform components. To achieve this goal, we first focus on all the privacy aspects for the data generated by our cloud game-based platform, by considering the European legal context for data privacy following GDPR and ISO/IEC TR 20748-1:2016 recommendations for Learning Analytics (LA). Our second objective is to provide implementation guidelines for efficient data privacy management for our cloud game-based educative platform. All these contributions are not found in current related works. The CyberScratch project, which was approved by UNED for the year 2020, considers using the xAPI standard for data handling and services for the game editor, game engine and game monitor modules of CyberScratch. Therefore, apart from considering GDPR privacy and LA recommendations, our cloud game-based architecture covers all phases from game creation to the final users’ interactions with the game.

scholarly journals PROTEKSI PRIVASI BIG DATA DALAM MEDIA SOSIAL

2020 ◽  
Vol 3 (1) ◽  
pp. 1-33
Author(s):  
Winarsih Winarsih ◽  
Irwansyah Irwansyah
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Service Providers ◽  
Meta Analysis ◽  
Privacy Management ◽  
Communication Privacy Management ◽  
The Public ◽  
Privacy Issues

AbstrakPerkembangan media sosial di Indonesia begitu pesat dengan jumlah pengguna yang  terus  meningkat.   Akan   tetapi  hal  tersebut   kurang  diimbangi   dengan kesadaran tentang privasi dalam kaitannya dengan big data yang dihasilkan oleh penyedia  layanan.  Penyedia  layanan  memberikan  kebijakan  berupa  syarat dan ketentuan  akan tetapi masyarakat  umumnya masih rendah dalam hal memiliki kesadaran  tentang privasi  data pribadi  mereka.  Penelitian  ini bertujuan  untuk mengetahui  solusi dari permasalahan  privasi  big data  dalam  media  sosial  dan dianalisis   dengan   teori  privasi   komunikasi.   Metode  yang  digunakan   dalam penelitian ini adalah metode meta-analisis yang mengolah hasil temuan dari penelitian sebelumnya. Hasil dari penelitian ini berupa solusi bagi perlindungan privasi data individu saat pembuatan, penyimpanan, dan pemrosesan data. Kata Kunci: data besar, Indonesia, kebijakan, media sosial, privasi AbstractThe development of social media in Indonesia is high increasing. However, this is not  accompanied   by  awareness   of  privacy  in  its  commitment   to  big  data generated  by service providers.  The service provider provides an agreed policy, will provide the public about their data privacy issues. This article used Communication Privacy Management to finding solution about big data privacy problems.   The  method  used  in  this  study  is  a  meta-analysis   method   that processes  the findings  from previous  studies.  The results  of this study contain solutions for privacy protection when creating data, data storage, and processing data. Keywords: big data, Indonesia, policy, social media, privacy

scholarly journals Efficient Data Deduplication Mechanism for Genomic Data

2019 ◽  
Vol 4 (2) ◽  
pp. 52-58
Author(s):  
Tin Thein Thwel . ◽  
G R Sinha .
Keyword(s):  
Data Storage ◽  
Data Science ◽  
Genomic Data ◽  
Integrated Approach ◽  
Human Beings ◽  
Data Deduplication ◽  
Huge Amount ◽  
Data Set ◽  
Single Instance ◽  
Efficient Data

During the data science age, many people tend to access health concerned information and diagnosis using information technology, including telemedicine. Therefore, many researchers attempting to work with medical experts as well as bioinformatics area. In the bioinformatics field, handling the genomic data of human beings becomes essential such as collecting, storing and processing. Genomic data refers to the genome and DNA data of an organism. Unavoidably, genomic data require huge amount of storage for the customized software to analyze. Recently, genome researchers are rising the alarms over big data.This research papers attempts in significant amount of reduction of data storage by applying data deduplication process in genomic data set. Data deduplication, ‘dedupe’ in short can reduce the amount of storage because of its single instance storage nature.Therefore, data deduplication becomes one of the solutions for optimizing the huge amount of storage spaces for genome storage.We have implemented data deduplication method and applied it to genomic data and the deduplication performed successfully by using secure hash algorithm, B++ tree and sub-file level chunking algorithm. The methods were implemented in integrated approach. The files are separated into different chunks with the help of Two Threshold Two Divisors algorithm and hash function is used to get chunk identifiers. Indexing keys are constructed using the identifiersin B+ tree like index structure.Thissystem can reduce the storage space significantly when there exist duplicated data. The preliminary testing is made using NCBI datasets

Cybersecurity and the new firm: surviving online threats

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vallari Chandna ◽  
Praneet Tiwari
Keyword(s):  
Risk Management ◽  
Data Privacy ◽  
Small Firms ◽  
Management Practices ◽  
Privacy Management ◽  
Privacy Risk ◽  
Entrepreneurial Ventures ◽  
Content Type ◽  
New Firm ◽  
Established Firms

Purpose Nascent firms and startups are often subject to challenges that their more mature counterparts can avoid. While cybersecurity is an issue that all firms contend with, it is especially challenging for new entrepreneurial ventures who lack the resources and capabilities of established firms. The purpose of this paper is to seek to delve deeper into the cybersecurity and risk management needs of small firms and startups. Design/methodology/approach Extant literature and available tools are explored to develop a usable framework applicable to small firms and new entrepreneurial ventures. Findings The liabilities of newness and smallness make entrepreneurial ventures a unique context in which to study the significance of cybersecurity and data privacy risk management. The authors offer an overview of issues and potential solutions relevant to entrepreneurial ventures. Research limitations/implications While offering practical insights, the work is a theoretical framework. The framework will enable researchers to develop more nuanced theory when it comes to cybersecurity and data privacy risk management. Practical implications The framework illustrates four distinct contexts for cybersecurity and risk management when it comes to the needs of small firms and startups. Adoption levels are explained, and small business operators and entrepreneurs can thus use the framework to determine the most appropriate approach for their enterprise. Originality/value The authors develop a framework illustrating adoption of different security and risk management practices by entrepreneurial ventures based on their specific needs and context. The authors thus offer practical solutions for startups and nascent firms regarding cybersecurity and privacy management.

PpBAC

2018 ◽  
Vol 30 (4) ◽  
pp. 14-31 ◽  
Author(s):  
Suyel Namasudra ◽  
Pinki Roy
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Data Storage ◽  
Control Method ◽  
Cloud Environment ◽  
Authorized User ◽  
Control Scheme ◽  
Control Schemes ◽  
Confidential Data ◽  
Efficient Data

This article describes how nowadays, cloud computing is one of the advanced areas of Information Technology (IT) sector. Since there are many hackers and malicious users on the internet, it is very important to secure the confidentiality of data in the cloud environment. In recent years, access control has emerged as a challenging issue of cloud computing. Access control method allows data accessing of an authorized user. Existing access control schemes mainly focus on the confidentiality of the data storage. In this article, a novel access control scheme has been proposed for efficient data accessing. The proposed scheme allows reducing the searching cost and accessing time, while providing the data to the user. It also maintains the security of the user's confidential data.

On efficient data storage service for IoT

2016 ◽  
Vol 27 (3) ◽  
pp. e1932 ◽  
Author(s):  
Konrad Karolewicz ◽  
Andrzej Beben ◽  
Jordi Mongay Batalla ◽  
George Mastorakis ◽  
Constandinos X. Mavromoustakis
Keyword(s):  
Data Storage ◽  
Storage Service ◽  
Efficient Data

scholarly journals Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7701
Author(s):  
Sayed-Chhattan Shah
Keyword(s):  
Machine Learning ◽  
Data Storage ◽  
Smart City ◽  
Data Privacy ◽  
Mobile Technologies ◽  
Cloud System ◽  
Multilayer Network ◽  
Network Applications ◽  
Data Rates ◽  
5G Network

Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments.

Sign in / Sign up

Export Citation Format

Share Document