Improved Security and Privacy In: A Media Cloud Computing Middleware for Content Management Using Cloud Material Protection Algorithm

2014 ◽  
Vol 02 (12) ◽  
pp. 7182-7189
Author(s):  
R.Bha gya ◽  
Chitra devi
Keyword(s):  
Cloud Computing ◽  
Content Management ◽  
Security And Privacy ◽  
Cloud Material ◽  
Media Cloud

Media cloud: an open cloud computing middleware for content management

2011 ◽  
Vol 57 (2) ◽  
pp. 970-978 ◽  
Author(s):  
Daniel Diaz-Sanchez ◽  
Florina Almenarez ◽  
Andres Marin ◽  
Davide Proserpio ◽  
Patricia Cabarcos
Keyword(s):  
Cloud Computing ◽  
Content Management ◽  
Media Cloud

Security and Privacy in Data Networks

2018 ◽  
pp. 1-20
Keyword(s):  
Cloud Computing ◽  
Mobile Applications ◽  
Personal Data ◽  
Smart Phones ◽  
Security And Privacy ◽  
Automated System ◽  
Virtual Human ◽  
Data Networks ◽  
Different Sources ◽  
Media Cloud

This article has as a purpose to deal with security and privacy of the data handled daily worldwide. It describes and analyzes the ways of violating private communications that make in various ways such as (Internet Activities, Smart Phones, Viruses, Hacking, Social Media, Cloud Computing, Bots, Mobile Applications, Internet of Things, Metadata, and Tracking / Surveillance). It analyzes the above mentioned and also trying to find countermeasures to protect the confidentiality and integrity of data. The collection and analysis of information nowadays is becoming more easily in different ways and from different sources to join all of them the information to create a virtual human profile becoming very easy. The freedoms of individuals have been reduced significantly in this contributed automated system in most cases without the consent of the users that record, store and process personal data including files unknowingly. This article aims to highlight the major problem of violation of the electronic data and privacy, to present countermeasures enriching knowledge from simple user until the advanced professional for the going on around and how it can defend itself.

scholarly journals A Novel Cloud Computing Algorithm of Security and Privacy

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Chih-Yung Chen ◽  
Jih-Fu Tu
Keyword(s):  
Cloud Computing ◽  
Information Security ◽  
Large Scale ◽  
Security And Privacy ◽  
Cloud Computing Security ◽  
Calculating Method ◽  
And Performance ◽  
Experimental Data Analysis ◽  
Cloud Material ◽  
Application Programs

The emergence of cloud computing has simplified the flow of large-scale deployment distributed system of software suppliers; when issuing respective application programs in a sharing clouds service to different user, the management of material becomes more complex. Therefore, in multitype clouds service of trust environment, when enterprises face cloud computing, what most worries is the issue of security, but individual users are worried whether the privacy material will have an outflow risk. This research has mainly analyzed several different construction patterns of cloud computing, and quite relevant case in the deployment construction security of cloud computing by fit and unfit quality, and proposed finally an optimization safe deployment construction of cloud computing and security mechanism of material protection calculating method, namely, Global Authentication Register System (GARS), to reduce cloud material outflow risk. We implemented a system simulation to test the GARS algorithm of availability, security and performance. By experimental data analysis, the solutions of cloud computing security, and privacy derived from the research can be effective protection in cloud information security. Moreover, we have proposed cloud computing in the information security-related proposals that would provide related units for the development of cloud computing security practice.

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

scholarly journals Past, present, and future of smart learning: a topic-based bibliometric analysis

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Xieling Chen ◽  
Di Zou ◽  
Haoran Xie ◽  
Fu Lee Wang
Keyword(s):  
Cloud Computing ◽  
Blended Learning ◽  
Information And Communication Technologies ◽  
Content Management ◽  
Research Field ◽  
Smart Devices ◽  
Major Research ◽  
Research Issues ◽  
Course Content ◽  
Smart Learning

AbstractInnovative information and communication technologies have reformed higher education from the traditional way to smart learning. Smart learning applies technological and social developments and facilitates effective personalized learning with innovative technologies, especially smart devices and online technologies. Smart learning has attracted increasing research interest from the academia. This study aims to comprehensively review the research field of smart learning by conducting a topic modeling analysis of 555 smart learning publications collected from the Scopus database. In particular, it seeks answers to (1) what the major research topics concerning smart learning were, and (2) how these topics evolved. Results demonstrate several major research issues, for example, Interactive and multimedia learning, STEM (science, technology, engineering, and mathematics) education, Attendance and attention recognition, Blended learning for smart learning, and Affective and biometric computing. Furthermore, several emerging topics were identified, for example, Smart learning analytics, Software engineering for e-learning systems, IoT (Internet of things) and cloud computing, and STEM education. Additionally, potential inter-topic directions were highlighted, for instance, Attendance and attention recognition and IoT and cloud computing, Semantics and ontology and Mobile learning, Feedback and assessment and MOOCs (massive open online courses) and course content management, as well as Blended learning for smart learning and Ecosystem and ambient intelligence.

Cloud computing security and privacy: Standards and regulations

2017 ◽  
Vol 54 ◽  
pp. 1-2 ◽  
Author(s):  
Yong Yu ◽  
Atsuko Miyaji ◽  
Man Ho Au ◽  
Willy Susilo
Keyword(s):  
Cloud Computing ◽  
Security And Privacy ◽  
Cloud Computing Security ◽  
Standards And Regulations

scholarly journals Data Security and Privacy in Cloud Computing

2014 ◽  
Vol 10 (7) ◽  
pp. 190903 ◽  
Author(s):  
Yunchuan Sun ◽  
Junsheng Zhang ◽  
Yongping Xiong ◽  
Guangyu Zhu
Keyword(s):  
Cloud Computing ◽  
Data Security ◽  
Security And Privacy

Proposed Back Propagation Deep Neural Network for Intrusion Detection in Internet of Things Fog Computing

2021 ◽  
Vol 9 (4) ◽  
pp. 464-469
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Fog Computing ◽  
Denial Of Service ◽  
Back Propagation ◽  
Security And Privacy ◽  
Brute Force ◽  
Iot Platform ◽  
Proposed Model

Internet of things (IoT) is an emerging concept which aims to connect billions of devices with each other anytime regardless of their location. Sadly, these IoT devices do not have enough computing resources to process huge amount of data. Therefore, Cloud computing is relied on to provide these resources. However, cloud computing based architecture fails in applications that demand very low and predictable latency, therefore the need for fog computing which is a new paradigm that is regarded as an extension of cloud computing to provide services between end users and the cloud user. Unfortunately, Fog-IoT is confronted with various security and privacy risks and prone to several cyberattacks which is a serious challenge. The purpose of this work is to present security and privacy threats towards Fog-IoT platform and discuss the security and privacy requirements in fog computing. We then proceed to propose an Intrusion Detection System (IDS) model using Standard Deep Neural Network's Back Propagation algorithm (BPDNN) to mitigate intrusions that attack Fog-IoT platform. The experimental Dataset for the proposed model is obtained from the Canadian Institute for Cybersecurity 2017 Dataset. Each instance of the attack in the dataset is separated into separate files, which are DoS (Denial of Service), DDoS (Distributed Denial of Service), Web Attack, Brute Force FTP, Brute Force SSH, Heartbleed, Infiltration and Botnet (Bot Network) Attack. The proposed model is trained using a 3-layer BP-DNN

Sign in / Sign up

Export Citation Format

Share Document