Seven principles to foster privacy and security in educational tools: Local Educational Data Analytics

2020 ◽  
Author(s):  
Daniel Amo ◽  
Ricardo Torres ◽  
Xavier Canaleta ◽  
Javier Herrero-Martín ◽  
Cristina Rodríguez-Merino ◽  
...  
Keyword(s):  
Data Analytics ◽  
Privacy And Security ◽  
Educational Tools ◽  
Seven Principles

scholarly journals Balancing Privacy Vs Efficiency in Data Analytics using Nearest Neighbour Randomization

2019 ◽  
Vol 8 (12) ◽  
pp. 2289-2295
Keyword(s):  
Data Analytics ◽  
Large Scale ◽  
Personal Data ◽  
Privacy And Security ◽  
Computing Platform ◽  
Use Of Data ◽  
Digital Era ◽  
Considerable Research ◽  
Group Data ◽  
Security Practices

The Digital era marked by the unrivalled growth of Internet and its services with day-to-day technological advancements has paved way for a data driven society. This digital explosion offers opportunities for extracting valuable information from collected data, which are used by organizations and research establishments for synergistic advantage. However, privacy of online divulged data is an issue that gets overlooked as a consequence of such large-scale analytics. Although, privacy and security practices conjointly determine the ethics of data collection and its use, personal data of individuals is largely at risk of disclosure. Considerable research has gone into privacy preserving analytics, in the light of Big Data and IoT boom, but scalable and efficient techniques, that do not compromise the usefulness of privacy constrained data, continues to be a challenging arena for research. The proposed work makes use of a distance-based perturbation method to group data and further randomizes data. The efficacy of perturbed data is evaluated for classification task that gives results on par with the non-perturbed counterpart. The relative performance of the algorithm is also evaluated on the parallel computing platform Spark. Results show that the technique does not hinder the use of data for holistic analysis while privacy is subjectively maintained.

scholarly journals Privacy-Aware Data Forensics of VRUs Using Machine Learning and Big Data Analytics

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Muhammad Babar ◽  
Muhammad Usman Tariq ◽  
Ahmed S. Almasoud ◽  
Mohammad Dahman Alshehri
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Traffic Control ◽  
Data Analytics ◽  
Data Privacy ◽  
Big Data Analytics ◽  
Processing Unit ◽  
Privacy And Security ◽  
User Data ◽  
Data Ingestion

The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results.

Big Data Management

2017 ◽  
Vol 7 (3) ◽  
pp. 44-55 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Industry ◽  
Business Leaders ◽  
Privacy And Security ◽  
Review Analysis ◽  
Using Data

The objective of this article is to provide the advanced issues and approaches of big data management. The literature review indicates the overview of big data management; the aspects of Big Data Analytics (BDA); the importance of big data management; the methods for big data management; the privacy and security concerns of big data management; and the big data management in the health care industry. Organizations that have been successful in working with effective big data management have accomplished this issue using data to help make sense of the information. The volume of data that companies are able to gather about customers and market conditions can provide business leaders with insights into new revenue and business opportunities, presuming they can spot the opportunities in vast amounts of data. The literature review analysis provides both practitioners and researchers an important understanding about big data management in modern organizations.

scholarly journals Secured Big Data Analytics for Decision-Oriented Medical System Using Internet of Things

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1273
Author(s):  
Amjad Rehman ◽  
Khalid Haseeb ◽  
Tanzila Saba ◽  
Jaime Lloret ◽  
Usman Tariq
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Medical Devices ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Medical System ◽  
The Internet ◽  
Healthcare Applications ◽  
Privacy And Security ◽  
Cloud Computing System

The Internet of Medical Things (IoMT) has shown incredible development with the growth of medical systems using wireless information technologies. Medical devices are biosensors that can integrate with physical things to make smarter healthcare applications that are collaborated on the Internet. In recent decades, many applications have been designed to monitor the physical health of patients and support expert teams for appropriate treatment. The medical devices are attached to patients’ bodies and connected with a cloud computing system for obtaining and analyzing healthcare data. However, such medical devices operate on battery powered sensors with limiting constraints in terms of memory, transmission, and processing resources. Many healthcare solutions are helping the community with the efficient monitoring of patients’ conditions using cloud computing, however, mostly incur latency in data collection and storage. Therefore, this paper presents a model for the Secured Big Data analytics using Edge–Cloud architecture (SBD-EC), which aims to provide distributed and timely computation of a decision-oriented medical system. Moreover, the mobile edges cooperate with the cloud level to present a secure algorithm, achieving reliable availability of medical data with privacy and security against malicious actions. The performance of the proposed model is evaluated in simulations and the results obtained demonstrate significant improvement over other solutions.

Risks in Adoption and Implementation of Big Data Analytics

2021 ◽  
Vol 10 (3) ◽  
pp. 1-11
Author(s):  
Rajasekhara Mouly Potluri ◽  
Narasimha Rao Vajjhala
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Data Privacy ◽  
Small And Medium Enterprises ◽  
Big Data Analytics ◽  
Small Companies ◽  
Privacy And Security ◽  
Adoption And Implementation ◽  
Research Outcome ◽  
Medium Enterprises

The research investigates the risks in adopting and implementing big data analytics in Indian micro, small, and medium enterprises (MSMEs). The researchers outlined a survey questionnaire for accumulating reactions from managers working in 50 Indian micro, small, and medium-sized enterprises on behalf of five vital commercial sectors. The application and use of big data analytics offer several significant problems for small companies as an investment in hardware and software resources are substantial. This study's findings provided experimental evidence on five critical challenges that Indian MSMEs face while adopting and implementing big data analytics: lack of human resources, data privacy and security, shortage of technological resources, deficiency of awareness, and financial implications. This study's findings emphasize the challenges that MSMEs face while leveraging big data analytics benefits. The research outcome will promote MSMEs' organizational leadership in planning and developing short-term and long-term information systems strategies.

scholarly journals Big Data Analytics in Medicine and Healthcare

2018 ◽  
Vol 15 (3) ◽  
Author(s):  
Blagoj Ristevski ◽  
Ming Chen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Data Privacy ◽  
Big Data Analytics ◽  
Heterogeneous Data ◽  
Distributed Data ◽  
Biomedical Data ◽  
Privacy And Security ◽  
Distributed Data Processing ◽  
Data Processing Software

Abstract This paper surveys big data with highlighting the big data analytics in medicine and healthcare. Big data characteristics: value, volume, velocity, variety, veracity and variability are described. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various – omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data. We underline the challenging issues about big data privacy and security. Regarding big data characteristics, some directions of using suitable and promising open-source distributed data processing software platform are given.

scholarly journals Big Healthcare Data: Survey of Challenges and Privacy

2020 ◽  
Vol 4 (4) ◽  
pp. 184
Author(s):  
Mohammed Bin Jubeir ◽  
Mohd Arfian Ismail ◽  
Shahreen Kasim ◽  
Hidra Amnur ◽  
Defni
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Healthcare Delivery ◽  
Big Data Analytics ◽  
Quality Of Healthcare ◽  
Care Providers ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Wide Range

The last century witnessed a dramatic leap in the shift towards digitizing the healthcare workflow and moving to e-patients' records. Health information is consistently becoming more diverse and complex, leading to the so-called massive data. Additionally, the demand for big data analytics in healthcare organizations is increasingly growing with the aim of providing a wide range of unprecedented potentials that are considered necessary for the provision of meaningful information about big data and improve the quality of healthcare delivery. It also aims to increase the effectiveness and efficiency of healthcare organizations; provide doctors and care providers better decision-making information and help them in the early detection of diseases. It also assists in evidence-based medicine and helps to minimize healthcare cost. However, a clear contradiction exists between the privacy and security of big data and its widespread usage. In this paper, the focus is on big data with respect to its characteristics, trends, and challenges. Additionally, the risks and benefits associated with data analytics were reviewed.

scholarly journals Survey of Security and Privacy Issues in Big Data Analytics

2019 ◽  
Vol 8 (S1) ◽  
pp. 33-35
Author(s):  
G. A. Mylavathi ◽  
N. M. Mallika ◽  
K. Mohanraj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Information Privacy ◽  
Security And Privacy ◽  
Network Services ◽  
Privacy And Security ◽  
Security Issues ◽  
Massive Information ◽  
Privacy Issues

Due to the reasons such as the rapid growth and spread of network services, mobile devices, and online users on the Internet leading to a remarkable increase in the amount of data. Almost each trade is making an attempt to address this large information. Big data phenomenon has begun to gain importance. However, it’s not solely terribly tough to store massive information and analyses them with ancient applications, however conjointly it’s difficult privacy and security issues. For this reason, this paper discusses the massive information, its scheme, considerations on massive information and presents comparative read of massive information privacy and security approaches in literature in terms of infrastructure, application, and data. By grouping these applications associate overall perspective of security and privacy problems in massive information is usually recommended

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