Teaching Workplace Privacy Issues with a Big Data Group Project

2020 ◽  
Vol 37 (2) ◽  
pp. 185-244
Author(s):  
Perry Binder ◽  
Susan L. Willey ◽  
Harold A. Weston
Keyword(s):  
Big Data ◽  
Group Project ◽  
Workplace Privacy ◽  
Privacy Issues ◽  
Data Group

Data Quality Associated with Big Data Processing: A Survey

2021 ◽  
Vol 23 (06) ◽  
pp. 1011-1018
Author(s):  
Aishrith P Rao ◽  
◽  
Raghavendra J C ◽  
Dr. Sowmyarani C N ◽  
Dr. Padmashree T ◽  
...  
Keyword(s):  
Big Data ◽  
Data Quality ◽  
Data Gathering ◽  
Cost Effective ◽  
Data Repository ◽  
Critical Approach ◽  
Critical Aspect ◽  
Quality Of Data ◽  
Data Group

With the advancement of technology and the large volume of data produced, processed, and stored, it is becoming increasingly important to maintain the quality of data in a cost-effective and productive manner. The most important aspects of Big Data (BD) are storage, processing, privacy, and analytics. The Big Data group has identified quality as a critical aspect of its maturity. Nonetheless, it is a critical approach that should be adopted early in the lifecycle and gradually extended to other primary processes. Companies are very reliant and drive profits from the huge amounts of data they collect. When its consistency deteriorates, the ramifications are uncertain and may result in completely undesirable conclusions. In the sense of BD, determining data quality is difficult, but it is essential that we uphold the data quality before we can proceed with any analytics. We investigate data quality during the stages of data gathering, preprocessing, data repository, and evaluation/analysis of BD processing in this paper. The related solutions are also suggested based on the elaboration and review of the proposed problems.

Opportunities and Challenges of Big Data in Healthcare

2020 ◽  
pp. 1989-2001
Author(s):  
Wafaa Faisal Mukhtar ◽  
Eltayeb Salih Abuelyaman
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Ethical Issues ◽  
Standard Of Care ◽  
Healthcare Sector ◽  
Useful Knowledge ◽  
Healthcare Data ◽  
Big Data Streams ◽  
Privacy Issues ◽  
Collection Methods

Healthcare big data streams from multiple information sources at an alarming volume, velocity, and variety. The challenge that faces the healthcare industry is extracting meaningful value from such sources. This chapter investigates the diversity and forms of data in the healthcare sector, reviews the methods used to search and analyze these data throughout the past years, and the use of machine learning and data mining techniques to mine useful knowledge from such data. The chapter will also highlight innovations of particular systems and tools which spot the fine approaches for different healthcare data, raise the standard of care and recap the tools and data collection methods. The authors emphasize some of ethical issues regarding processing these records and some data privacy issues.

Big Data and the Internet of Things

2021 ◽  
pp. 1321-1342
Author(s):  
Zablon Pingo ◽  
Bhuva Narayan
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Predictive Analytics ◽  
Negative Impact ◽  
Consumer Products ◽  
Informational Privacy ◽  
Protection Mechanisms ◽  
Iot Devices ◽  
Aid Decision ◽  
Privacy Issues

The privacy construct is an important aspect of internet of things (IoT) technologies as it is projected that over 20 billion IoT devices will be in use by 2022. Among other things, IoT produces big data and many industries are leveraging this data for predictive analytics to aid decision making in health, education, business, and other areas. Despite benefits in some areas, privacy issues have persisted in relation to the use of the data produced by many consumer products. The practices surrounding IoT and Big Data by service providers and third parties are associated with a negative impact to individuals. To protect consumers' privacy, a wide range of approaches to informational privacy protections exist. However, individuals are increasingly required to actively respond to control and manage their informational privacy rather than rely on any protection mechanisms. This chapter highlights privacy issues across consumers' use of IoT and identifies existing responses to enhance privacy awareness as a way of enabling IoT users to protect their privacy.

scholarly journals Toward a Literature-Driven Definition of Big Data in Healthcare

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Emilie Baro ◽  
Samuel Degoul ◽  
Régis Beuscart ◽  
Emmanuel Chazard
Keyword(s):  
Big Data ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Data Reuse ◽  
Secondary Use ◽  
Meaningful Information ◽  
Fields Of Study ◽  
Study Characteristics ◽  
Definition Of ◽  
Privacy Issues

Objective.The aim of this study was to provide a definition of big data in healthcare.Methods.A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals(n)and the number of variables(p)for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed.Results.A total of 196 papers were included. Big data can be defined as datasets withLog⁡(n*p)≥7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues.Conclusion.Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.

scholarly journals Big data management: Security and privacy concerns

2021 ◽  
Vol 8 (5) ◽  
pp. 73-83
Author(s):  
Ibrahim A. Atoum ◽  
◽  
Ismail M. Keshta ◽  
Keyword(s):  
Big Data ◽  
Data Management ◽  
Large Data ◽  
Security And Privacy ◽  
Data Sets ◽  
Privacy Concerns ◽  
Software Companies ◽  
Systemic Analysis ◽  
Functional Areas ◽  
Privacy Issues

Big data has been used by different companies to deliver simple products and provide enhanced customer insights through predictive technology such as artificial intelligence. Big data is a field that mainly deals with the extraction and systemic analysis of large data sets to help businesses discover trends. Today, many companies use Big Data to facilitate growth in different functional areas as well as expand their ability to handle large customer databases. Big data has grown the demand for information management experts such that many software companies are increasingly investing in firms that specialize in data management and analytics. Nevertheless, the issue of data protection or privacy is a threat to big data management. This article presents some of the major concerns surrounding the application and use of Big Data about challenges of security and privacy of data stored on technological devices. The paper also discusses some of the current studies being undertaken aimed at addressing security and privacy issues in Big Data.

scholarly journals Improving the data access control using blockchain for healthcare domain

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 901
Author(s):  
Olaosebikan Tahir Yinka ◽  
Su-Cheng Haw ◽  
Timothy Tzen Vun Yap ◽  
Samini Subramaniam
Keyword(s):  
Big Data ◽  
Identity Management ◽  
Performance Metrics ◽  
Data Access ◽  
Data Access Control ◽  
Proposed Model ◽  
Big Data Technologies ◽  
Access To Data ◽  
Authorization Control ◽  
Privacy Issues

Introduction: Unauthorized access to data is one of the most significant privacy issues that hinder most industries from adopting big data technologies. Even though specific processes and structures have been put in place to deal with access authorization and identity management for large databases nonetheless, the scalability criteria are far beyond the capabilities of traditional databases. Hence, most researchers are looking into other solutions, such as big data management. Methods: In this paper, we firstly study the strengths and weaknesses of implementing cryptography and blockchain for identity management and authorization control in big data, focusing on the healthcare domain. Subsequently, we propose a decentralized data access and sharing system that preserves privacy to ensure adequate data access management under the blockchain. In addition, we designed a blockchain framework to resolve the decentralized data access and sharing system privacy issues, by implementing a public key infrastructure model, which utilizes a signature cryptography algorithm (elliptic curve and signcryption). Lastly, we compared the proposed blockchain model to previous techniques to see how well it performed. Results: We evaluated the blockchain on four performance metrics which include throughput, latency, scalability, and security. The proposed blockchain model was tested using a sample of 5000 patients and 500,000 observations. The performance evaluation results further showed that the proposed model achieves higher throughput and lower latency compared to existing approaches when the workload varies up to 10,000 transactions. Discussion: This research reviews the importance of blockchains as they provide infinite possibilities to individuals, companies, and governments.

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