A vision for global privacy bridges: Technical and legal measures for
international data markets
- URL: http://arxiv.org/abs/2005.06324v1
- Date: Wed, 13 May 2020 13:55:50 GMT
- Title: A vision for global privacy bridges: Technical and legal measures for
international data markets
- Authors: Sarah Spiekermann, Alexander Novotny
- Abstract summary: Despite data protection laws and an acknowledged right to privacy, trading personal information has become a business equated with "trading oil"
An open conflict is arising between business demands for data and a desire for privacy.
We propose and test a vision of a personal information market with privacy.
- Score: 77.34726150561087
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: From the early days of the information economy, personal data has been its
most valuable asset. Despite data protection laws and an acknowledged right to
privacy, trading personal information has become a business equated with
"trading oil". Most of this business is done without the knowledge and active
informed consent of the people. But as data breaches and abuses are made public
through the media, consumers react. They become irritated about companies' data
handling practices, lose trust, exercise political pressure and start to
protect their privacy with the help of technical tools. As a result, companies'
Internet business models that are based on personal data are unsettled. An open
conflict is arising between business demands for data and a desire for privacy.
As of 2015 no true answer is in sight of how to resolve this conflict.
Technologists, economists and regulators are struggling to develop technical
solutions and policies that meet businesses' demand for more data while still
maintaining privacy. Yet, most of the proposed solutions fail to account for
market complexity and provide no pathway to technological and legal
implementation. They lack a bigger vision for data use and privacy. To break
this vicious cycle, we propose and test such a vision of a personal information
market with privacy. We accumulate technical and legal measures that have been
proposed by technical and legal scholars over the past two decades. And out of
this existing knowledge, we compose something new: a four-space market model
for personal data.
Related papers
- Collection, usage and privacy of mobility data in the enterprise and public administrations [55.2480439325792]
Security measures such as anonymization are needed to protect individuals' privacy.
Within our study, we conducted expert interviews to gain insights into practices in the field.
We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy.
arXiv Detail & Related papers (2024-07-04T08:29:27Z) - SoK: The Gap Between Data Rights Ideals and Reality [46.14715472341707]
Do rights-based privacy laws effectively empower individuals over their data?
This paper scrutinizes these approaches by reviewing empirical studies, news articles, and blog posts.
arXiv Detail & Related papers (2023-12-03T21:52:51Z) - Big Data Privacy in Emerging Market Fintech and Financial Services: A Research Agenda [0.9310318514564271]
White paper describes a research agenda to advance our understanding of the problem and solution of data privacy in emerging market and financial services.
We highlight five priority areas for research: comprehensive analyses; understanding local definitions of data privacy'; documenting key sources of risk, and potential technical solutions.
We hope this research agenda will focus attention on the multi-faceted nature of privacy in emerging markets.
arXiv Detail & Related papers (2023-10-08T02:11:19Z) - A Critical Take on Privacy in a Datafied Society [0.0]
I analyze several facets of the lack of online privacy and idiosyncrasies exhibited by privacy advocates.
I discuss of possible effects of datafication on human behavior, the prevalent market-oriented assumption at the base of online privacy, and some emerging adaptation strategies.
A glimpse on the likely problematic future is provided with a discussion on privacy related aspects of EU, UK, and China's proposed generative AI policies.
arXiv Detail & Related papers (2023-08-03T11:45:18Z) - Protecting User Privacy in Online Settings via Supervised Learning [69.38374877559423]
We design an intelligent approach to online privacy protection that leverages supervised learning.
By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user.
arXiv Detail & Related papers (2023-04-06T05:20:16Z) - Second layer data governance for permissioned blockchains: the privacy
management challenge [58.720142291102135]
In pandemic situations, such as the COVID-19 and Ebola outbreak, the action related to sharing health data is crucial to avoid the massive infection and decrease the number of deaths.
In this sense, permissioned blockchain technology emerges to empower users to get their rights providing data ownership, transparency, and security through an immutable, unified, and distributed database ruled by smart contracts.
arXiv Detail & Related papers (2020-10-22T13:19:38Z) - Online publication of court records: circumventing the
privacy-transparency trade-off [0.0]
We argue that current practices are insufficient for coping with massive access to legal data.
We propose a straw man multimodal architecture paving the way to a full-fledged privacy-preserving legal data publishing system.
arXiv Detail & Related papers (2020-07-03T13:58:01Z) - The Challenges and Impact of Privacy Policy Comprehension [0.0]
This paper experimentally manipulated the privacy-friendliness of an unavoidable and simple privacy policy.
Half of our participants miscomprehended even this transparent privacy policy.
To mitigate such pitfalls we present design recommendations to improve the quality of informed consent.
arXiv Detail & Related papers (2020-05-18T14:16:48Z) - Beyond privacy regulations: an ethical approach to data usage in
transportation [64.86110095869176]
We describe how Federated Machine Learning can be applied to the transportation sector.
We see Federated Learning as a method that enables us to process privacy-sensitive data, while respecting customer's privacy.
arXiv Detail & Related papers (2020-04-01T15:10:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.