The Ethical Issues of Location-Based Services on Big Data and IoT

2021 ◽  
pp. 195-205
Author(s):  
Victor Chang ◽  
Yeqing Mou ◽  
Qianwen Ariel Xu
Keyword(s):  
Big Data ◽  
Ethical Issues ◽  
Location Based Services

scholarly journals Big Data: Some Ethical Concerns for the Social Sciences

2021 ◽  
Vol 10 (2) ◽  
pp. 36
Author(s):  
Michael Weinhardt
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Ethical Issues ◽  
Online Behavior ◽  
Ethical Concerns ◽  
Ethical Implications ◽  
The Social ◽  
And Behaviors ◽  
State Actors

While big data (BD) has been around for a while now, the social sciences have been comparatively cautious in its adoption for research purposes. This article briefly discusses the scope and variety of BD, and its research potential and ethical implications for the social sciences and sociology, which derive from these characteristics. For example, BD allows for the analysis of actual (online) behavior and the analysis of networks on a grand scale. The sheer volume and variety of data allow for the detection of rare patterns and behaviors that would otherwise go unnoticed. However, there are also a range of ethical issues of BD that need consideration. These entail, amongst others, the imperative for documentation and dissemination of methods, data, and results, the problems of anonymization and re-identification, and the questions surrounding the ability of stakeholders in big data research and institutionalized bodies to handle ethical issues. There are also grave risks involved in the (mis)use of BD, as it holds great value for companies, criminals, and state actors alike. The article concludes that BD holds great potential for the social sciences, but that there are still a range of practical and ethical issues that need addressing.

scholarly journals A Healthy Sense of Trust

2017 ◽  
Vol 2 (Suppl. 1) ◽  
pp. 1-10
Author(s):  
Denis Horgan
Keyword(s):  
European Union ◽  
Clinical Trials ◽  
Big Data ◽  
Data Protection ◽  
Fast Moving ◽  
Ethical Issues ◽  
Personalised Medicine ◽  
Drug Pricing ◽  
The Right ◽  
The Eu

In the fast-moving arena of modern healthcare with its cutting-edge science it is already, and will become more, vital that stakeholders collaborate openly and effectively. Transparency, especially on drug pricing, is of paramount importance. There is also a need to ensure that regulations and legislation covering, for example the new, smaller clinical trials required to make personalised medicine work effectively, and the huge practical and ethical issues surrounding Big Data and data protection, are common, understood and enforced across the EU. With more integration, collaboration, dialogue and increased trust among each and every one in the field, stakeholders can help mould the right frameworks, in the right place, at the right time. Once achieved, this will allow us all to work more quickly and more effectively towards creating a healthier - and thus wealthier - European Union.

Big Data and Privacy State of the Art

2019 ◽  
pp. 104-158
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

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 Privacy State of the Art

2021 ◽  
pp. 947-991
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Big Data from Management Perspective

2016 ◽  
pp. 92-106
Author(s):  
Alireza Bolhari
Keyword(s):  
Social Media ◽  
Big Data ◽  
Ethical Issues ◽  
Banking System ◽  
Mobile Sensors ◽  
Complex Data ◽  
Management Perspective ◽  
Public Sectors

Competency matters. Social media, customer transactions, mobile sensors, and feedback contents are all piled up with data. This might be unstructured and complex data in voluminous quantity, often called Big Data. However, if this Big Data is managed, it might bring competency for organizations. This chapter introduces the must-know concepts and materials for organizational managers who face Big Data. Through the chapter, Big Data is defined and its emergence over the time is reviewed. The four Vs model in Big Data literature and its link to a banking system is analyzed. The chapter concludes by making a managerial awareness concerning ethical issues in Big Data. This is of high priority in public sectors as data relies for every individual in the society.

scholarly journals Openness in Big Data and Data Repositories

2019 ◽  
Vol 11 (3) ◽  
pp. 255-273 ◽  
Author(s):  
Vicki Xafis ◽  
Markus K. Labude
Keyword(s):  
Big Data ◽  
Ethical Issues ◽  
Open Science ◽  
Research Data ◽  
Data Repositories ◽  
Ethics Framework ◽  
Full Explanation ◽  
Health Related Research ◽  
Health Related ◽  
Privacy And Confidentiality

Abstract There is a growing expectation, or even requirement, for researchers to deposit a variety of research data in data repositories as a condition of funding or publication. This expectation recognizes the enormous benefits of data collected and created for research purposes being made available for secondary uses, as open science gains increasing support. This is particularly so in the context of big data, especially where health data is involved. There are, however, also challenges relating to the collection, storage, and re-use of research data. This paper gives a brief overview of the landscape of data sharing via data repositories and discusses some of the key ethical issues raised by the sharing of health-related research data, including expectations of privacy and confidentiality, the transparency of repository governance structures, access restrictions, as well as data ownership and the fair attribution of credit. To consider these issues and the values that are pertinent, the paper applies the deliberative balancing approach articulated in the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of Openness in Big Data and Data Repositories. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.

scholarly journals Cross-Sectoral Big Data

2019 ◽  
Vol 11 (3) ◽  
pp. 327-339 ◽  
Author(s):  
Graeme T. Laurie
Keyword(s):  
Big Data ◽  
Ethical Issues ◽  
Health Sector ◽  
Well Being ◽  
Biomedical Data ◽  
Ethical Values ◽  
Ethics Framework ◽  
Full Explanation ◽  
Health And Well Being

Abstract Discussion of uses of biomedical data often proceeds on the assumption that the data are generated and shared solely or largely within the health sector. However, this assumption must be challenged because increasingly large amounts of health and well-being data are being gathered and deployed in cross-sectoral contexts such as social media and through the internet of (medical) things and wearable devices. Cross-sectoral sharing of data thus refers to the generation, use and linkage of biomedical data beyond the health sector. This paper considers the challenges that arise from this phenomenon. If we are to benefit fully, it is important to consider which ethical values are at stake and to reflect on ways to resolve emerging ethical issues across ecosystems where values, laws and cultures might be quite distinct. In considering such issues, this paper applies the deliberative balancing approach of the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of cross-sectoral big data. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.

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