A Taxonomy of Decentralized Identifier Methods for Practitioners
October 18, 2023 Β· The Cartographer Β· π IEEE International Conference on Decentralized Applications and Infrastructures
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"Title-pattern auto-detect: A Taxonomy of Decentralized Identifier Methods for Practitioners"
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Authors
Felix Hoops, Alexander MΓΌhle, Florian Matthes, Christoph Meinel
arXiv ID
2311.03367
Category
cs.SE: Software Engineering
Citations
6
Venue
IEEE International Conference on Decentralized Applications and Infrastructures
Last Checked
3 days ago
Abstract
A core part of the new identity management paradigm of Self-Sovereign Identity (SSI) is the W3C Decentralized Identifiers (DIDs) standard. The diversity of interoperable implementations encouraged by the paradigm is key for a less centralized future, and it is made possible by the concept of DIDs. However, this leads to a kind of dilemma of choices, where practitioners are faced with the difficult decision of which methods to choose and support in their applications. Due to the decentralized development of DID method specifications and the overwhelming number of different choices, it is hard to get an overview. In this paper, we propose a taxonomy of DID methods with the goal to empower practitioners to make informed decisions when selecting DID methods. To that end, our taxonomy is designed to provide an overview of the current landscape while providing adoption-relevant characteristics. For this purpose, we rely on the Nickerson et al. methodology for taxonomy creation, utilizing both conceptual-to-empirical and empirical-to-conceptual approaches. During the iterative process, we collect and survey an extensive and potentially exhaustive list of around 160 DID methods from various sources. The taxonomy we arrive at uses a total of 7 dimensions and 22 characteristics to span the contemporary design space of DID methods from the perspective of a practitioner. In addition to elaborating on these characteristics, we also discuss how a practitioner can use the taxonomy to select suitable DID methods for a specific use case.
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