In Light of the Legal Debate over Personal Data Privacy at a Time of Globalized Big Data: Making Big Data Researchers Cooperating with Lawmakers to Find Solutions for the Future

Author(s):  
Francis Rousseaux ◽  
Pierre Saurel
2021 ◽  
Vol 4 ◽  
Author(s):  
Vibhushinie Bentotahewa ◽  
Chaminda Hewage ◽  
Jason Williams

The growing dependency on digital technologies is becoming a way of life, and at the same time, the collection of data using them for surveillance operations has raised concerns. Notably, some countries use digital surveillance technologies for tracking and monitoring individuals and populations to prevent the transmission of the new coronavirus. The technology has the capacity to contribute towards tackling the pandemic effectively, but the success also comes at the expense of privacy rights. The crucial point to make is regardless of who uses and which mechanism, in one way another will infringe personal privacy. Therefore, when considering the use of technologies to combat the pandemic, the focus should also be on the impact of facial recognition cameras, police surveillance drones, and other digital surveillance devices on the privacy rights of those under surveillance. The GDPR was established to ensure that information could be shared without causing any infringement on personal data and businesses; therefore, in generating Big Data, it is important to ensure that the information is securely collected, processed, transmitted, stored, and accessed in accordance with established rules. This paper focuses on Big Data challenges associated with surveillance methods used within the COVID-19 parameters. The aim of this research is to propose practical solutions to Big Data challenges associated with COVID-19 pandemic surveillance approaches. To that end, the researcher will identify the surveillance measures being used by countries in different regions, the sensitivity of generated data, and the issues associated with the collection of large volumes of data and finally propose feasible solutions to protect the privacy rights of the people, during the post-COVID-19 era.


Author(s):  
Nancy Victor ◽  
Daphne Lopez

Data privacy plays a noteworthy part in today's digital world where information is gathered at exceptional rates from different sources. Privacy preserving data publishing refers to the process of publishing personal data without questioning the privacy of individuals in any manner. A variety of approaches have been devised to forfend consumer privacy by applying traditional anonymization mechanisms. But these mechanisms are not well suited for Big Data, as the data which is generated nowadays is not just structured in manner. The data which is generated at very high velocities from various sources includes unstructured and semi-structured information, and thus becomes very difficult to process using traditional mechanisms. This chapter focuses on the various challenges with Big Data, PPDM and PPDP techniques for Big Data and how well it can be scaled for processing both historical and real-time data together using Lambda architecture. A distributed framework for privacy preservation in Big Data by combining Natural language processing techniques is also proposed in this chapter.


2019 ◽  

Our beautiful, new digital world has a come at a price, which we are paying by relinquishing our personal data–while we are shopping, driving our cars, and chatting and surfing on the Internet. However, the intelligent algorithms needed to process this data pose a threat to freedom in our society. They analyse and evaluate us, while predicting our behaviour. Big data and data mining are the business models of the future. What does all this mean for politics, the economy, journalism and political communication? Do we have to defend basic human rights and human dignity against the digital revolution? Do we need new laws and a code of ethics for algorithms? And how will politics, the media and democracy function under these new conditions? In this book, experts from a variety of academic fields, journalism and politics discuss these questions in terms of the future and society. With contributions by Johanna Haberer, Yvonne Hofstetter, Sabine Leutheusser-Schnarrenberger, Klaus Mainzer, Daniel Moßbrucker, Peter Schaar, Michael Schröder, Axel Schwanebeck and Thomas Zeilinger.


Author(s):  
Nancy Victor ◽  
Daphne Lopez

Data privacy plays a noteworthy part in today's digital world where information is gathered at exceptional rates from different sources. Privacy preserving data publishing refers to the process of publishing personal data without questioning the privacy of individuals in any manner. A variety of approaches have been devised to forfend consumer privacy by applying traditional anonymization mechanisms. But these mechanisms are not well suited for Big Data, as the data which is generated nowadays is not just structured in manner. The data which is generated at very high velocities from various sources includes unstructured and semi-structured information, and thus becomes very difficult to process using traditional mechanisms. This chapter focuses on the various challenges with Big Data, PPDM and PPDP techniques for Big Data and how well it can be scaled for processing both historical and real-time data together using Lambda architecture. A distributed framework for privacy preservation in Big Data by combining Natural language processing techniques is also proposed in this chapter.


2021 ◽  
Vol 23 ◽  
Author(s):  
Aidan Jack Katsikas ◽  
Vimal Murugan

Companies like Facebook and Google track their users’ data to enhance their ads and offer highly targeted ad space. The question is then raised: how much more profitable does collecting data make companies and what is the future of these companies, as rising data privacy concerns are believed to result in the passage of future legislation? In order to isolate the effects of collecting data on profit, the revenue and revenue growth of Google and Facebook were compared to their competitors that do not collect data and revenue was adjusted to find the average per user. Google was compared to another search engine, DuckDuckGo, and Facebook was compared to a social media site, Yubo. Our analysis found that both Google and Facebook earned significantly larger amounts of revenue, per user, than their competitors who do not collect data. However, DuckDuckGo and Yubo both experienced considerably larger revenue growth this past year, highlighting improved success among companies relying on different models for generating revenue. Our research and similar studies will become more important once legislation is passed, as companies may have to pay taxes or fees associated with acquiring users’ data.


MedienJournal ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 50-61 ◽  
Author(s):  
Jan Jagodzinski

This paper will first briefly map out the shift from disciplinary to control societies (what I call designer capitalism, the idea of control comes from Gilles Deleuze) in relation to surveillance and mediation of life through screen cultures. The paper then shifts to the issues of digitalization in relation to big data that have the danger of continuing to close off life as zoë, that is life that is creative rather than captured via attention technologies through marketing techniques and surveillance. The last part of this paper then develops the way artists are able to resist the big data archive by turning the data in on itself to offer viewers and participants a glimpse of the current state of manipulating desire and maintaining copy right in order to keep the future closed rather than being potentially open.


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