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Dynamics in Data Privacy and Sharing Economics | IEEE Journals & Magazine | IEEE Xplore

Dynamics in Data Privacy and Sharing Economics


Abstract:

As human beings, we continuously consume products, services, as well as produce products and services for others to consume. Due to our inherent bias of likes and dislike...Show More

Abstract:

As human beings, we continuously consume products, services, as well as produce products and services for others to consume. Due to our inherent bias of likes and dislikes, we unintentionally or intentionally end up liking products and/or services that are personalized for us, or somehow unintentionally match our likes. This personalization is influenced by the data gathered about us directly or indirectly. The richer the data is about us, the more personalized products and services we receive, thereby saving us time and money while meeting our desired goals for experience or exchange of value for the offers we are wanting. We remain forever concerned about the direct or accidental use of our data that can fall into the hands of nefarious users of the dark Web, or criminals who can cause us all kinds of harm using the data we shared. Herein lies the paradox of personalization and privacy. To solve this paradox, we acknowledge that there has to be a tradeoff between data privacy and personalization, and an optimized match on trust, with a value between the data owners (data subjects) and the data buyers. In this article, we discuss the dynamics, with tradeoffs, and set-up for these to play out in an optimal way specific to each of us.
Published in: IEEE Transactions on Technology and Society ( Volume: 2, Issue: 3, September 2021)
Page(s): 114 - 115
Date of Publication: 04 May 2021
Electronic ISSN: 2637-6415

I. Introduction

In this article, we seek to treat data as a wealth asset which belongs to the person or entity who generates the data, herein called the data owner or data subject. We use concepts like a marketplace, market dynamics in the marketplace, data privacy controls, open architecture for data, data wealth asset management, data locker, data wallet, and subscriptions on the data to propose a foundation for solving the paradox.

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References

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