Legal Responses to the Commodification of Personal Data in the Era of Big Data

2016 ◽  
pp. 130-139
Author(s):  
Emile Douilhet ◽  
Argyro P. Karanasiou
Keyword(s):  
Big Data ◽  
Personal Data ◽  
Raw Data ◽  
New Information ◽  
Legal Responses ◽  
Key Players ◽  
Data Market ◽  
Legislative Framework ◽  
The Relationship ◽  
Legal Grounds

Big Data is a relatively recent phenomenon, but has already shown its potential to drastically alter the relationship between businesses, individuals, and governments. Many organisations now control vast amounts of raw data, and those industry players with the resources to mine that data to create new information have a significant advantage in the big data market. The aim of this chapter is to identify the legal grounds for the ownership of big data: who legally owns the petabytes and exabytes of information created daily? Does this belong to the users, the data analysts, or to the data brokers and various infomediaries? The chapter presents a succinct overview of the legal ownership of big data by examining the key players in control of the information at each stage of processing of big data. It then moves on to describe the current legislative framework with regard to data protection and concludes in additional techno-legal solutions offered to complement the law of big data in this respect.

Legal Responses to the Commodification of Personal Data in the Era of Big Data

Web Services ◽  
2019 ◽  
pp. 2076-2085
Author(s):  
Emile Douilhet ◽  
Argyro P. Karanasiou
Keyword(s):  
Big Data ◽  
Personal Data ◽  
Raw Data ◽  
New Information ◽  
Legal Responses ◽  
Key Players ◽  
Data Market ◽  
Legislative Framework ◽  
The Relationship ◽  
Legal Grounds

Big Data is a relatively recent phenomenon, but has already shown its potential to drastically alter the relationship between businesses, individuals, and governments. Many organisations now control vast amounts of raw data, and those industry players with the resources to mine that data to create new information have a significant advantage in the big data market. The aim of this chapter is to identify the legal grounds for the ownership of big data: who legally owns the petabytes and exabytes of information created daily? Does this belong to the users, the data analysts, or to the data brokers and various infomediaries? The chapter presents a succinct overview of the legal ownership of big data by examining the key players in control of the information at each stage of processing of big data. It then moves on to describe the current legislative framework with regard to data protection and concludes in additional techno-legal solutions offered to complement the law of big data in this respect.

Evaluation of Big Data within the Context of the Protection of Personal Data and Competition Law

2020 ◽  
pp. 219-246
Author(s):  
Fatma Arvas
Keyword(s):  
Big Data ◽  
Conflict Of Interest ◽  
Personal Data ◽  
Competition Law ◽  
Market Strategy ◽  
Legal Principles ◽  
Legal Problems ◽  
Personal Rights ◽  
Foreign Law ◽  
The Relationship

In order to sustain their presence in the market, personal data acquirers must obtain, store, and process personal data from sources that feed data such as social media shares, shopping records, and sensor networks. On the other hand, the problem of conformity of real and legal persons whose personal data are processed within the framework of personal data law brings about many legal problems and requires a profound research rather than a limited examination. The fact that a small number of enterprises create dominant power by using big data in market strategy has led to data-dependent companies or markets. Due to the effects that big data caused in the market, there is a need to address many problems in the field of competition law as well as the dimension of privacy and personal rights. In this context, the conflict of interest between the economic interest created by big data and the legal principles of personal data will be addressed and compared with regard to Turkish law and foreign law practices by discussing the relationship between big data and competition law.

scholarly journals The forthcoming era of precision medicine

2016 ◽  
Vol 45 (2) ◽  
pp. 144
Author(s):  
Stjepan Gamulin
Keyword(s):  
Health Care ◽  
Medical Education ◽  
Big Data ◽  
Precision Medicine ◽  
Personal Data ◽  
Legislative Framework ◽  
Regulatory Bodies ◽  
Tailored Approach ◽  
Biomedical Community ◽  
Prevention And Therapy

<p><strong>Abstract</strong>. The aim of this essay is to present the definition and principles of personalized or precision medicine, the perspective and barriers to its development and clinical application. The implementation of precision medicine in health care requires the coordinated efforts of all health care stakeholders (the biomedical community, government, regulatory bodies, patients’ groups). Particularly, translational research with the integration of genomic and comprehensive data from all levels of the organism (“big data”), development of bioinformatics platforms enabling network analysis of disease etiopathogenesis, development of a legislative framework for handling personal data, and new paradigms of medical education are necessary for successful application of the concept of precision medicine in health care. <strong>Conclusion</strong>. In the present and future era of precision medicine, the collaboration of all participants in health care is necessary for its realization, resulting in improvement of diagnosis, prevention and therapy, based on a holistic, individually tailored approach.</p>

scholarly journals Uso de correo electrónico para analizar la comunicación bilateral aplicando Big Data y Regresión Lineal Simple

2019 ◽  
pp. 21-28
Author(s):  
Luz María Hernández-Cruz ◽  
Diana Concepción Mex-Alvarez ◽  
Guadalupe Manuel Estrada-Segovia ◽  
Margarita Castillo-Tellez
Keyword(s):  
Big Data ◽  
Linear Regression ◽  
Personal Data ◽  
Simple Linear Regression ◽  
Productive Labor ◽  
Working Groups ◽  
Microsoft Office ◽  
The Relationship ◽  
Academic Group

Currently, the email is the most used network service as a means of communication for sending and receiving messages and files. The objective of this study is to perform an analysis of institutional emails by applying a strategic that ensures the existence of a bilateral communication between the employees. The research is of applied type, which will allow to predict assertive working groups with prosperous and productive labor relations. The study integrates the application of a Technological Big Data tool called Immersion and the analysis of a Simple Linear Regression (PLS) model using Microsoft Office Excel. The adapted methodology is composed of three phases: first, the "Data Collection" where a large volume of data is collected (personal data) from an institutional email account for the case study, then we have the "Analysis" where a simple linear regression model is constructed to analyze the relationship between the collected data and finally, the "Interpretation" where the obtained results are explained. Having important applications such as the integration of academic group, thematic networks, disciplinary committees or collaborative members in projects.

scholarly journals Datafied Citizens in the Age of Coerced Digital Participation

2019 ◽  
Vol 24 (3) ◽  
pp. 414-429 ◽  
Author(s):  
Veronica Barassi
Keyword(s):  
Big Data ◽  
First Generation ◽  
Personal Data ◽  
Private And Public ◽  
New Type ◽  
Economic Infrastructure ◽  
Data Surveillance ◽  
The Uk ◽  
Political Economic ◽  
The Relationship

This article explores the relationship between surveillance capitalism, big data, and the emergence of a new type of datafied citizenship by looking at two different, yet interconnected, dimensions. In the first place, it considers how under surveillance capitalism individuals are being profiled simultaneously as consumer and citizen subjects by a complex political economic infrastructure that brings private and public entities together. In the second place, it argues that surveillance capitalism depends on the systematic coercion of digital participation, which forces citizens to comply with data technologies and give up their personal data. If we want to understand the extent of these transformation, the article argues, we need to look at children. Children have traditionally been excluded from debates about citizenship because they have often been understood as not-yet citizens or future citizens. Yet, in the study of the relationship between data and citizenship, children today are the key. They are the very first generation of citizens who are datafied from before they are born and are coerced into digitally participating to society through the data traces produced, collected, and processed by others without their consent or control. Drawing on the findings of the Child | Data | Citizen project, an ethnographically informed research project on big data and family life in the UK and US, this article will highlight some of the democratic challenges that emerge when we think about data, surveillance capitalism, and citizenship in everyday life.

scholarly journals Key Points for an Ethical Evaluation of Healthcare Big Data

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 493 ◽  
Author(s):  
Pilar Leon-Sanz
Keyword(s):  
Big Data ◽  
Ethical Issues ◽  
Group Identification ◽  
Life Sciences ◽  
Personal Data ◽  
Use Of Data ◽  
New Concepts ◽  
Key Points ◽  
Bioethical Debate ◽  
The Relationship

Background: The article studies specific ethical issues arising from the use of big data in Life Sciences and Healthcare. Methods: Main consensus documents, other studies, and particular cases are analyzed. Results: New concepts that emerged in five key areas for the bioethical debate on big data and health are identified—the accuracy and validity of data and algorithms, questions related to transparency and confidentiality in the use of data; aspects that raise the coding or pseudonymization and the anonymization of data, and also problems derived from the possible individual or group identification; the new ways of obtaining consent for the transfer of personal data; the relationship between big data and the responsibility of professional decision; and the commitment of the Institutions and Public Administrations. Conclusions: Good practices in the management of big data related to Life Sciences and Healthcare depend on respect for the rights of individuals, the improvement that these practices can introduce in assistance to individual patients, the promotion of society’s health in general and the advancement of scientific knowledge.

scholarly journals Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges

2020 ◽  
Vol 12 (24) ◽  
pp. 10571
Author(s):  
Jahoon Koo ◽  
Giluk Kang ◽  
Young-Gab Kim
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Big Data Analytics ◽  
Life Cycles ◽  
Security And Privacy ◽  
Personal Privacy ◽  
Security Issues ◽  
New Information ◽  
Data Life Cycle ◽  
Data Market

The use of big data in various fields has led to a rapid increase in a wide variety of data resources, and various data analysis technologies such as standardized data mining and statistical analysis techniques are accelerating the continuous expansion of the big data market. An important characteristic of big data is that data from various sources have life cycles from collection to destruction, and new information can be derived through analysis, combination, and utilization. However, each phase of the life cycle presents data security and reliability issues, making the protection of personally identifiable information a critical objective. In particular, user tendencies can be analyzed using various big data analytics, and this information leads to the invasion of personal privacy. Therefore, this paper identifies threats and security issues that occur in the life cycle of big data by confirming the current standards developed by international standardization organizations and analyzing related studies. In addition, we divide a big data life cycle into five phases (i.e., collection, storage, analytics, utilization, and destruction), and define the security taxonomy of the big data life cycle based on the identified threats and security issues.

Exploring the Influences of Service Characteristics on Individuals’Use Intention of Mobile Health Services (Preprint)

2020 ◽  
Author(s):  
Fei Liu ◽  
Pengkun Wu ◽  
Xitong Guo
Keyword(s):  
Personal Information ◽  
New Technology ◽  
Information Service ◽  
Health Care Research ◽  
Privacy Concern ◽  
Service Adoption ◽  
Service Characteristics ◽  
Research Areas ◽  
New Information ◽  
The Relationship

BACKGROUND Service characteristic factors are verified as the determinants for influencing people’s use intention of mHealth. Exploration of the interactions among the service characteristics of users can play an important role in improving service adoption rate. mHealth service appears to be an emerging new technology that presents a new pattern of healthcare service; however, users have concerns that their personal information might be disclosed and used without permission. This concern hinders people’s adoption behavior of mHealth services. OBJECTIVE The objective of this study is to explore how service characteristics (service relevance and service accuracy) interact to influence individuals’ use intention of mHealth services. This study also investigates the moderating roles of innovativeness and privacy concern. METHODS To meet these objectives, six hypotheses thus developed were empirically validated using a survey to test the effects of service characteristics and personal traits on use intention of mHealth. RESULTS We confirm that service relevance and service accuracy positively and directly influence individuals’ use intention of mHealth services. In addition, innovativeness positively affects the relationship between service relevance and use intention. Privacy concern negatively influences the relationship between service relevance and use intention, but positively influences the relationship between service accuracy and use intention. CONCLUSIONS The present study provides new insights into the influencing factors of individuals’ usage behaviour toward mHealth services. Such insight could provide further understanding of how individuals adopt new information service or technologies, which contribute to both information system and health care research areas in a very promising way.

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