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Kirchhoff-Law Johnson Noise Meets Web 3.0: A Statistical Physical Method of Random Key Generation for Decentralized Identity Protocols

2023/12/28 by Christiana Chamon, Chamon, Christiana, Kamalesh Mohanasundar +4 · 2 citations
Computer Science · Biochemistry, Genetics and Molecular Biology · #Advanced Statistical Modeling Techniques #Fractal and DNA sequence analysis

paper · pdf · doi:10.48550/arxiv.2312.17113

Abstract

This paper presents a statistical physical generation of random keys for a decentralized identity ecosystem that uses Web 3.0 protocols. Web 3.0 is driven by secure keys, typically represented in hexadecimal, that are pseudo-randomly generated by an initialization vector and complex computational algorithms. We demonstrate that the statistical physical Kirchhoff-law-Johnson-noise (KLJN) scheme eliminates the additional computational power by naturally generating truly random binary keys to drive the creation of decentralized identifiers (DIDs) that are appended to an Ethereum blockchain.

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