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The market for self-sovereign data
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Ng, Irene C. L. (2021) The market for self-sovereign data. Working Paper. Coventry: Warwick Manufacturing Group. WMG Service Systems Research Group Working Paper Series, 01/21 . (Unpublished)
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Abstract
This paper describes the design of a decentralized, real-time, digital market for private information in a property system where individuals can hold entitlements to the information ie. self sovereign data. It describes the current system where individuals cannot hold entitlements and the challenges it presents, as it would require the focus on the information itself and the rights over it. In a property system where individuals can hold entitlements, the challenge is the bargaining solution and scalable market structures. The paper presents a market design and implementation for the new property system and a pricing model for the information within the market.
| Item Type: | Working or Discussion Paper (Working Paper) | |||||||||
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| Subjects: | H Social Sciences > HF Commerce K Law [Moys] > KA Jurisprudence |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
| Library of Congress Subject Headings (LCSH): | Intellectual property , Data protection | |||||||||
| Series Name: | WMG Service Systems Research Group Working Paper Series | |||||||||
| Publisher: | Warwick Manufacturing Group | |||||||||
| Place of Publication: | Coventry | |||||||||
| ISSN: | 2049-4297 | |||||||||
| Official Date: | November 2021 | |||||||||
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| Volume: | 01/21 | |||||||||
| Number of Pages: | 11 | |||||||||
| Institution: | University of Warwick | |||||||||
| Status: | Not Peer Reviewed | |||||||||
| Publication Status: | Unpublished | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Description: | Version 2.0 |
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| Date of first compliant deposit: | 3 January 2020 | |||||||||
| Date of first compliant Open Access: | 8 January 2020 | |||||||||
| Funder: | This paper acknowledges the funding received under the Alan Turing Institute Fellowship program (2019-21) and the £1.1m UKRI grant funded program “Dynamic, Real Time, On-Demand Personalisation for Scaling (DROPS). EP/R033838/1 | |||||||||
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