scholarly journals Anticipatory Governance in Biobanking: Security and Risk Management in Digital Health

2021 ◽  
Vol 27 (3) ◽  
Author(s):  
Dagmar Rychnovská
Keyword(s):  
Big Data ◽  
Data Security ◽  
Digital Health ◽  
Life Sciences ◽  
Personal Data ◽  
Field Research ◽  
Societal Implications ◽  
Biomedical Data ◽  
Anticipatory Governance ◽  
Future Challenges

AbstractAlthough big-data research has met with multiple controversies in diverse fields, political and security implications of big data in life sciences have received less attention. This paper explores how threats and risks are anticipated and acted on in biobanking, which builds research repositories for biomedical samples and data. Focusing on the biggest harmonisation cluster of biomedical research in Europe, BBMRI-ERIC, the paper analyses different logics of risk in the anticipatory discourse on biobanking. Based on document analysis, interviews with ELSI experts, and field research, three types of framing of risk are reconstructed: data security, privacy, and data misuse. The paper finds that these logics downplay the broader social and political context and reflects on the limits of the practices of anticipatory governance in biobanking. It argues that this regime of governance can make it difficult for biobanks to address possible future challenges, such as access to biomedical data by authorities, pressures for integrating biobank data with other type of personal data, or their use for profiling beyond medical purposes. To address potential controversies and societal implications related to the use of big data in health research and medicine, the paper suggests to expand the vocabulary and practices of anticipatory governance, in the biobanking community and beyond.

scholarly journals Is Big Data Security Essential for Students to Understand?

2020 ◽  
Vol 11 (2) ◽  
pp. 161-170
Author(s):  
Rochman Hadi Mustofa
Keyword(s):  
Big Data ◽  
Undergraduate Students ◽  
Data Security ◽  
Personal Data ◽  
Digital Data ◽  
Security And Privacy ◽  
Group Discussions ◽  
Technology Applications ◽  
Digital Payments ◽  
Financial Technology

AbstractBig Data has become a significant concern of the world, along with the era of digital transformation. However, there are still many young people, especially in developing countries, who are not yet aware of the security of their big data, especially personal data. Misuse of information from big data often results in violations of privacy, security, and cybercrime. This study aims to determine how aware of the younger generation of security and privacy of their big data. Data were collected qualitatively by interviews and focus group discussions (FGD) from. Respondents were undergraduate students who used social media and financial technology applications such as online shopping, digital payments, digital wallet and hotel/transportation booking applications. The results showed that students were not aware enough and understood the security or privacy of their digital data, and some respondents even gave personal data to potentially scam sites. Most students are not careful in providing big data information because they are not aware of the risks behind it, socialization is needed in the future as a step to prevent potential data theft.

scholarly journals Medical research, Big Data and the need for privacy by design

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171882435 ◽  
Author(s):  
Bart Jacobs ◽  
Jean Popma
Keyword(s):  
Big Data ◽  
Medical Research ◽  
Scientific Community ◽  
Medical Center ◽  
Life Sciences ◽  
Personal Data ◽  
Research Data ◽  
Privacy By Design ◽  
Research Setting ◽  
Large Cohort Study

Medical research data is sensitive personal data that needs to be protected from unauthorized access and unintentional disclosure. In a research setting, sharing of (big) data within the scientific community is necessary in order to make progress and maximize scientific benefits derived from valuable and costly data. At the same time, convincingly protecting the privacy of people (patients) participating in medical research is a prerequisite for maintaining trust and willingness to share. In this commentary, we will address this issue and the pitfalls involved in the context of the PEP project 1 that provides the infrastructure for the Personalized Parkinson’s Project, 2 a large cohort study on Parkinson’s disease from Radboud University Medical Center (Radboudumc), in cooperation with Verily life Sciences, an Alphabet subsidiary.

scholarly journals From Big Data to Artificial Intelligence: chemoinformatics meets new challenges

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Igor V. Tetko ◽  
Ola Engkvist
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Chemical Industry ◽  
Life Sciences ◽  
Biomedical Data ◽  
Special Session ◽  
New Methods ◽  
New Challenges

Abstract The increasing volume of biomedical data in chemistry and life sciences requires development of new methods and approaches for their analysis. Artificial Intelligence and machine learning, especially neural networks, are increasingly used in the chemical industry, in particular with respect to Big Data. This editorial highlights the main results presented during the special session of the International Conference on Neural Networks organized by “Big Data in Chemistry” project and draws perspectives on the future progress of the field. Graphical Abstract

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 Patient information ownership in the age of digital health and big data

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
M Mirchev
Keyword(s):  
Public Health ◽  
Big Data ◽  
Data Analytics ◽  
Personal Information ◽  
Digital Health ◽  
Clinical Care ◽  
Personal Data ◽  
Health Data ◽  
Patient Data ◽  
Long Run

Abstract Background In the context of digital health and the increasing capabilities to derive, store and use information, Big data, and data analytics provide an exceptional perspective towards the evolution of medicine and public health. We collect patient data at unimaginable scale thanks to technological improvements such as wearables, sensors, smart and mobile devices. We are digitizing health on our way to improve cares. The other side of the coin reveals specific issues: it is all about personal information. The risks we face in regard to privacy, autonomy and ultimately justice are worth debating. Aim To consider whether ownership of patient data in the context of digital health and Big data is a good way to guarantee both privacy and the social interest in the field of public health. Methods Historical, documental, ethical research. Results The abilities to collect and store zettabytes of health-related information is spectacular, but learning how to structure and optimize the use of this information is pivotal for the future of public health. People are sensitive in terms of “ownership”, rights and privacy, although the idea for actual ownership of health information is not quite popular. Given the fact, that it is personal data, a lot of concerns are related to ensuring privacy. One way to do it is by recognizing patient ownership over their data. The major issue with this, is that it might limit, or even prevent public interest, and so the public benefits. Having in mind the huge commercial interest in health data, that concern looks relevant. When applied in healthcare Big data has the potential to provide important data analytics, which means that we can move to next step in healthcare development - improving disease prevention and health promotion, which are vastly ignored in favor of clinical care. In this specific environment, it is highly questionable whether patient`s ownership would bring more benefit, than harms in the shared goal of improving healthcare. Key messages What people might do if their health data is their property, might reflect in a bad way the common goal to structure and use it for health improving. Patient data ownership might not be reasonable in the long run, even though from an ethical standpoint and with regard to patient`s autonomy looks fair.

The use of Big Data: A Russian perspective of personal data security

2017 ◽  
Vol 33 (4) ◽  
pp. 482-501 ◽  
Author(s):  
Anna Konstantinovna Zharova ◽  
Vladimir Mikhailovich Elin
Keyword(s):  
Big Data ◽  
Data Security ◽  
Personal Data

Big Data Security Challenges and Solution of Distributed Computing in Hadoop Environment: A Security Framework

2020 ◽  
Vol 13 (4) ◽  
pp. 790-797
Author(s):  
Gurjit Singh Bhathal ◽  
Amardeep Singh Dhiman
Keyword(s):  
Big Data ◽  
Data Security ◽  
Data Sets ◽  
Security Framework ◽  
Hadoop Distributed File System ◽  
Current Scenario ◽  
Hadoop Cluster ◽  
Ciphertext Policy ◽  
In Transit ◽  
Hadoop Framework

Background: In current scenario of internet, large amounts of data are generated and processed. Hadoop framework is widely used to store and process big data in a highly distributed manner. It is argued that Hadoop Framework is not mature enough to deal with the current cyberattacks on the data. Objective: The main objective of the proposed work is to provide a complete security approach comprising of authorisation and authentication for the user and the Hadoop cluster nodes and to secure the data at rest as well as in transit. Methods: The proposed algorithm uses Kerberos network authentication protocol for authorisation and authentication and to validate the users and the cluster nodes. The Ciphertext-Policy Attribute- Based Encryption (CP-ABE) is used for data at rest and data in transit. User encrypts the file with their own set of attributes and stores on Hadoop Distributed File System. Only intended users can decrypt that file with matching parameters. Results: The proposed algorithm was implemented with data sets of different sizes. The data was processed with and without encryption. The results show little difference in processing time. The performance was affected in range of 0.8% to 3.1%, which includes impact of other factors also, like system configuration, the number of parallel jobs running and virtual environment. Conclusion: The solutions available for handling the big data security problems faced in Hadoop framework are inefficient or incomplete. A complete security framework is proposed for Hadoop Environment. The solution is experimentally proven to have little effect on the performance of the system for datasets of different sizes.

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