مقاله انگلیسی رایگان در مورد حریم خصوصی و امنیت در پارادایم کلان داده – تیلور و فرانسیس ۲۰۱۸

مقاله انگلیسی رایگان در مورد حریم خصوصی و امنیت در پارادایم کلان داده – تیلور و فرانسیس ۲۰۱۸

 

مشخصات مقاله
انتشار مقاله سال ۲۰۱۸
تعداد صفحات مقاله انگلیسی ۱۱ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه تیلور و فرانسیس
نوع مقاله ISI
عنوان انگلیسی مقاله Privacy and security in the big data paradigm
ترجمه عنوان مقاله حریم خصوصی و امنیت در پارادایم کلان داده
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی کامپیوتر
گرایش های مرتبط امنیت اطلاعات
مجله مجله سیستم های اطلاعات کامپیوتری – Journal of Computer Information Systems
دانشگاه PNG University of Technology – Papua New Guinea
کلمات کلیدی کلان داده؛ پارادایم کلان داده ؛ حریم خصوصی و امنیت؛ مراقبت های بهداشتی؛ روند ویبول نمايي؛ آنالیز کلان داده
کلمات کلیدی انگلیسی Big data; big data paradigm; privacy and security; healthcare; exponential Weibull trend; big data analytics
کد محصول E6865
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Introduction

Big data is a new paradigm with almost all of the scholarly literature having emerged since 2012.1,2 Privacy and security are important for everyone in the age of big data. For example, one concerns the abuse of medical privacy through leaking personal information to the employer so that the hiring decisions have been made unfairly based on the medical history. 2 Big data privacy and security risks are hot topics in the media as well as in academic research.3–۷ From an external risk perspective, there were multiple occurrences of cyber security hackers breaking into multiple private electronic databases, linking fields together, and subsequently leveraging that data to obtain confidential information. 7–۱۱ For example, in the USA, a “hacker believed to be tied to the Russian intelligence services made public another set of internal Democratic Party documents, including the personal cellphone numbers and email addresses of nearly 200 lawmakers”. ۱۲ Some big data privacy dilemmas originated internally as revealed by several well-known American company misadventures, namely Orbitz, Netflix, and Target.13,14 BBC reported that a massive cyber-attack using ransomware struck organizations of 99 countries around the world in May 2017 15 Among the worst hit was the National Health Service (NHS) in England and Scotland. Then about 40 NHS organizations and some medical practices cancelled operations and appointments. NHS staff shared screenshots of the WannaCry program, which is a ransomware demanding a payment of $300 (£۲۳۰) in virtual currency Bitcoin to unlock the files for each infected computer. 15 This latest cyber-attack to hospitals implies that there is a need for more research into privacy and security in the big data paradigm. Furthermore, cyber-attacks against the hospital’s power infrastructure and water supply and ransomware to shut down the hospital servers imply that healthcare big data privacy and security are an important yet unpopulated body of knowledge 11,17–۱۹, because hospitals’ servers could significantly impact patient care and healthcare. 16 There have been a significant number of research publications on big data privacy and security.2,7,11 However, the majority of recent literature states that we need more research on big data privacy and security (e.g. 8–۱۰,۱۳,۱۵,۱۷–۲۱). However, they have not further classified privacy and security in the big data age although ACM provided a classification of privacy and security in a general setting in 2012. 22This is a compelling justification to examine the status of privacy and security research in the big data paradigm. Based on this above discussion, the following three issues are significant for privacy and security in the big data paradigm: (1) What are the interrelationships among privacy, security, and big data? (2) How can we classify big data-driven privacy and security? (3) What is the status of privacy and security research in the big data paradigm and the state-of-the-art privacy and security in the big data age? This paper will address these three issues. More specifically, it addresses the first issue by presenting a Boolean model for big data privacy and security based on the Boolean algebra. It addresses the second issue by presenting a classification of big data-driven privacy and security based on the characteristics of big data and literature review. It addresses the third issue by providing a statistical analysis of big data and its relationships with privacy and security based on the literature data published from 1916 to 2016. It also addresses the third issue by analysis of SCOPUS searched data (2012–۲۰۱۶). The rest of this paper is organized as follows. Section 2 overviews the approach and methodology for this research. Section 3 proposes a Boolean model for big data privacy and security. Section 4 presents a classification of big data-driven privacy and security. Section 5 provides a statistical analysis of big data-driven privacy and security. Section 6 looks at the state-of-the-art privacy and security in the big data age. Section 7 discusses the implications and limitations of this research, and the final section summarizes the findings and proposes recommendations for future research.

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