scholarly journals From Patients to Petabytes: Genomic Big Data, Privacy, and Informational Risk

2014 ◽  
Vol 39 (4) ◽  
Author(s):  
Julie Frizzo-Barker ◽  
Peter Chow-White
Keyword(s):  
Big Data ◽  
Human Body ◽  
Science And Technology Studies ◽  
Data Privacy ◽  
Personal Information ◽  
Digital Health ◽  
Social Constructions ◽  
Genomic Databases ◽  
The Social ◽  
Core Areas

Genomic big data is an emerging information technology, which presents new opportunities for medical innovation as well as new challenges to our current ethical, social and legal infrastructure. Rapid, affordable whole genomic sequencing translates patients’ most sensitive personal information into petabytes of digital health data. While a biomedical approach traditionally focuses on risks and benefits to the human body, the fields of communication and science and technology studies (STS) can provide some of the critical and theoretical tools necessary to navigate the newly emerging terrain of the human body as digital code. Core areas of expertise from these fields including the Internet, the network society and the social constructions of technology ground our discussion of the social implications of open access genomic databases, privacy and informational risk.Le « Big Data » en génomique est une technologie de l’information émergente, qui offre de nouvelles possibilités pour l’innovation médicale et présente de nouveaux défis pour nos structures éthique, sociale et juridique. Un séquençage génomique rapide et abordable, convertit les renseignements personnels les plus sensibles des patients en pétaoctets de données numériques de santé. Tandis que l’approche biomédicale traditionnellement se concentre sur les risques et les bénéfices pour la santé, les Études de la Communication, de la Science et de la Technologie (STS) peuvent fournir certains outils critiques et théoriques nécessaires afin d’explorer le terrain émergent de la représentation numérique du corps humain. Les domaines principaux de ces champs d’étude dont l’Internet, la société en réseau et les constructions sociales de la technologie, forment la base de notre discussion sur les implications sociales de l’accès ouvert aux bases de données génomiques, la confidentialité et les risques liés au stockage et la diffusion de l’information.

scholarly journals Bought and Sold: Exploring the Effects of Big Data on User Agency and Commodification

2021 ◽  
Author(s):  
Kristia M. Pavlakos
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Privacy ◽  
Large Data ◽  
Data Sets ◽  
Privacy Regulation ◽  
Scholarly Literature ◽  
User Interests ◽  
The Social ◽  
The Relationship

Big Data1is a phenomenon that has been increasingly studied in the academy in recent years, especially in technological and scientific contexts. However, it is still a relatively new field of academic study; because it has been previously considered in mainly technological contexts, more attention needs to be drawn to the contributions made in Big Data scholarship in the social sciences by scholars like Omar Tene and Jules Polonetsky, Bart Custers, Kate Crawford, Nick Couldry, and Jose van Dijk. The purpose of this Major Research Paper is to gain insight into the issues surrounding privacy and user rights, roles, and commodification in relation to Big Data in a social sciences context. The term “Big Data” describes the collection, aggregation, and analysis of large data sets. While corporations are usually responsible for the analysis and dissemination of the data, most of this data is user generated, and there must be considerations regarding the user’s rights and roles. In this paper, I raise three main issues that shape the discussion: how users can be more active agents in data ownership, how consent measures can be made to actively reflect user interests instead of focusing on benefitting corporations, and how user agency can be preserved. Through an analysis of social sciences scholarly literature on Big Data, privacy, and user commodification, I wish to determine how these concepts are being discussed, where there have been advancements in privacy regulation and the prevention of user commodification, and where there is a need to improve these measures. In doing this, I hope to discover a way to better facilitate the relationship between data collectors and analysts, and user-generators. 1 While there is no definitive resolution as to whether or not to capitalize the term “Big Data”, in capitalizing it I chose to conform with such authors as boyd and Crawford (2012), Couldry and Turow (2014), and Dalton and Thatcher (2015), who do so in the scholarly literature.

Consumer Privacy Regulations

2021 ◽  
pp. 1844-1860
Author(s):  
Martha Davis
Keyword(s):  
Big Data ◽  
Consumer Protection ◽  
Data Privacy ◽  
Personal Information ◽  
Smart Cities ◽  
Consumer Trust ◽  
Consumer Privacy ◽  
Societal Benefits ◽  
Globalized Economy ◽  
Information Consumers

Big data and analytics have not only changed how businesses interact with consumers, but also how consumers interact with the larger world. Smart cities, IoT, cloud, and edge computing technologies are all enabled by data and can provide significant societal benefits via efficiencies and reduction of waste. However, data breaches have also caused serious harm to customers by exposing personal information. Consumers often are unable to make informed decisions about their digital privacy because they are in a position of asymmetric information. There are an increasing number of privacy regulations to give consumers more control over their data. This chapter provides an overview of data privacy regulations, including GDPR. In today's globalized economy, the patchwork of international privacy regulations is difficult to navigate, and, in many instances, fails to provide adequate business certainty or consumer protection. This chapter also discusses current research and implications for costs, data-driven innovation, and consumer trust.

scholarly journals Bought and Sold: Exploring the Effects of Big Data on User Agency and Commodification

2021 ◽  
Author(s):  
Kristia M. Pavlakos
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Privacy ◽  
Large Data ◽  
Data Sets ◽  
Privacy Regulation ◽  
Scholarly Literature ◽  
User Interests ◽  
The Social ◽  
The Relationship

Big Data1is a phenomenon that has been increasingly studied in the academy in recent years, especially in technological and scientific contexts. However, it is still a relatively new field of academic study; because it has been previously considered in mainly technological contexts, more attention needs to be drawn to the contributions made in Big Data scholarship in the social sciences by scholars like Omar Tene and Jules Polonetsky, Bart Custers, Kate Crawford, Nick Couldry, and Jose van Dijk. The purpose of this Major Research Paper is to gain insight into the issues surrounding privacy and user rights, roles, and commodification in relation to Big Data in a social sciences context. The term “Big Data” describes the collection, aggregation, and analysis of large data sets. While corporations are usually responsible for the analysis and dissemination of the data, most of this data is user generated, and there must be considerations regarding the user’s rights and roles. In this paper, I raise three main issues that shape the discussion: how users can be more active agents in data ownership, how consent measures can be made to actively reflect user interests instead of focusing on benefitting corporations, and how user agency can be preserved. Through an analysis of social sciences scholarly literature on Big Data, privacy, and user commodification, I wish to determine how these concepts are being discussed, where there have been advancements in privacy regulation and the prevention of user commodification, and where there is a need to improve these measures. In doing this, I hope to discover a way to better facilitate the relationship between data collectors and analysts, and user-generators. 1 While there is no definitive resolution as to whether or not to capitalize the term “Big Data”, in capitalizing it I chose to conform with such authors as boyd and Crawford (2012), Couldry and Turow (2014), and Dalton and Thatcher (2015), who do so in the scholarly literature.

A Secure Protocol for High-Dimensional Big Data Providing Data Privacy

2021 ◽  
pp. 327-343
Author(s):  
Anitha J. ◽  
Prasad S. P.
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Personal Information ◽  
Technological Development ◽  
High Dimensional ◽  
Sensitive Information ◽  
Data Anonymization ◽  
Secure Protocol ◽  
Data Owner

Due to recent technological development, a huge amount of data generated by social networking, sensor networks, internet, etc., adds more challenges when performing data storage and processing tasks. During PPDP, the collected data may contain sensitive information about the data owner. Directly releasing this for further processing may violate the privacy of the data owner, hence data modification is needed so that it does not disclose any personal information. The existing techniques of data anonymization have a fixed scheme with a small number of dimensions. There are various types of attacks on the privacy of data like linkage attack, homogeneity attack, and background knowledge attack. To provide an effective technique in big data to maintain data privacy and prevent linkage attacks, this paper proposes a privacy preserving protocol, UNION, for a multi-party data provider. Experiments show that this technique provides a better data utility to handle high dimensional data, and scalability with respect to the data size compared with existing anonymization techniques.

A Secure Protocol for High-Dimensional Big Data Providing Data Privacy

2020 ◽  
pp. 347-363
Author(s):  
Anitha J. ◽  
Prasad S. P.
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Personal Information ◽  
Technological Development ◽  
High Dimensional ◽  
Sensitive Information ◽  
Data Anonymization ◽  
Secure Protocol ◽  
Data Owner

Due to recent technological development, a huge amount of data generated by social networking, sensor networks, internet, etc., adds more challenges when performing data storage and processing tasks. During PPDP, the collected data may contain sensitive information about the data owner. Directly releasing this for further processing may violate the privacy of the data owner, hence data modification is needed so that it does not disclose any personal information. The existing techniques of data anonymization have a fixed scheme with a small number of dimensions. There are various types of attacks on the privacy of data like linkage attack, homogeneity attack, and background knowledge attack. To provide an effective technique in big data to maintain data privacy and prevent linkage attacks, this paper proposes a privacy preserving protocol, UNION, for a multi-party data provider. Experiments show that this technique provides a better data utility to handle high dimensional data, and scalability with respect to the data size compared with existing anonymization techniques.

Privacy Prevention in Healthcare Data Using Big Data

2019 ◽  
Vol 16 (8) ◽  
pp. 3576-3581
Author(s):  
R. Aroul Canessane ◽  
J. Albert Mayan ◽  
R. DhanaLakshmi ◽  
Ragini Singh ◽  
Sushmita Bhowmik
Keyword(s):  
Big Data ◽  
Biomedical Research ◽  
Data Privacy ◽  
Personal Information ◽  
Healthcare Research ◽  
Individual Data ◽  
Relevant Paper ◽  
Healthcare Data ◽  
Big Data Privacy ◽  
Patient’S Information

The use of the patient’s information in biomedical research or healthcare research is increasing rapidly. We are using big data to generate and collect a large amount of personal information of patients. The security of patients individual data have turned into an extraordinary threat as it might prompt spillage of delicate data which can put the patient’s protection in danger. There are various measures which have been taken to protect the data from attack. The relevant paper reviews relevant topics in the context of healthcare research. We will discuss the consequences of big data privacy in healthcare research and a better way to improve the data privacy in healthcare research or biomedical research.

Combination of Access Control and De-Identification for Privacy Preserving in Big Data

2016 ◽  
Vol 10 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Amine Rahmani ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Big Data ◽  
Access Control ◽  
Data Privacy ◽  
Scientific Community ◽  
High Efficiency ◽  
Personal Information ◽  
Privacy Preserving ◽  
Big Data Privacy ◽  
Basic Concepts ◽  
Identification Techniques

Despite of its emergence and advantages in various domains, big data still suffers from major disadvantages. Timeless, scalability, and privacy are the main problems that hinder the advance of big data. Privacy preserving has become a wide search era within the scientific community. This paper covers the problem of privacy preserving over big data by combining both access control and data de-identification techniques in order to provide a powerful system. The aim of this system is to carry on all big data properties (volume, variety, velocity, veracity, and value) to ensure protection of users' identities. After many experiments and tests, our system shows high efficiency on detecting and hiding personal information while maintaining the utility of useful data. The remainder of this report is addressed in the presentation of some known works over a privacy preserving domain, the introduction of some basic concepts that are used to build our approach, the presentation of our system, and finally the display and discussion of the main results of our experiments.

Consumer Privacy Regulations

2020 ◽  
pp. 222-238
Author(s):  
Martha Davis
Keyword(s):  
Big Data ◽  
Consumer Protection ◽  
Data Privacy ◽  
Personal Information ◽  
Smart Cities ◽  
Consumer Trust ◽  
Consumer Privacy ◽  
Societal Benefits ◽  
Globalized Economy ◽  
Information Consumers

Big data and analytics have not only changed how businesses interact with consumers, but also how consumers interact with the larger world. Smart cities, IoT, cloud, and edge computing technologies are all enabled by data and can provide significant societal benefits via efficiencies and reduction of waste. However, data breaches have also caused serious harm to customers by exposing personal information. Consumers often are unable to make informed decisions about their digital privacy because they are in a position of asymmetric information. There are an increasing number of privacy regulations to give consumers more control over their data. This chapter provides an overview of data privacy regulations, including GDPR. In today's globalized economy, the patchwork of international privacy regulations is difficult to navigate, and, in many instances, fails to provide adequate business certainty or consumer protection. This chapter also discusses current research and implications for costs, data-driven innovation, and consumer trust.

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.

Sign in / Sign up

Export Citation Format

Share Document