Comparative Analysis of Decentralized Identity Approaches


When trust and performance cannot be placed in the hands of a single organization, decentralization is an absolute necessity. Examples of Distributed Ledger Technologies (DLTs) and Decentralized Hash Tables (DHTs) include situations in which the former is useful for transactional events while the latter is useful for the storage of large amounts of data. These two technologies, when combined, have the potential to solve a variety of problems. The Blockchain is a distributed ledger technology (DLT) that has a history that cannot be changed and is protected by cryptographic signatures in data blocks. Identification is a fundamental concern that is typically met by centralized trust anchors. Self-sovereign identities, also known as SSIs, are decentralized models that were recently proposed. With the help of DHT, users are able to control and manage their identities. On the other hand, a problem that arises with decentralized identification systems is slowness. This is because there are many connections and requests made by participants. This article focuses on decentralized identification using DLT and DHT, which allows users to control the information about themselves and store their biometrics. We take a look at some of the other options that are currently available and tackle the problem of poor performance by analyzing the performance of various decentralized identification methods based on execution time and throughput. We demonstrate that the DHT and Machine Learning model (BioIPFS) is superior to other solutions such as uPort, ShoCard, and BBID in terms of its overall performance.

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