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ZENODO
Dataset . 2018
License: CC 0
Data sources: ZENODO
DRYAD
Dataset . 2018
License: CC 0
Data sources: Datacite
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Data from: Committing to quantum resistance: a slow defence for Bitcoin against a fast quantum computing attack

Authors: Stewart, Iain; Ilie, Dragos; Zamyatin, Alexei; Werner, Sam; Torshizi, Maryam F.; Knottenbelt, William J.;

Data from: Committing to quantum resistance: a slow defence for Bitcoin against a fast quantum computing attack

Abstract

Quantum computers are expected to have a dramatic impact on numerous fields, due to their anticipated ability to solve classes of mathematical problems much more efficiently than their classical counterparts. This particularly applies to domains involving integer factorisation and discrete logarithms, such as public key cryptography. In this paper we consider the threats a quantum-capable adversary could impose on Bitcoin, which currently uses the Elliptic Curve Digital Signature Algorithm (ECDSA) to sign transactions. We then propose a simple but slow commit--delay--reveal protocol, which allows users to securely move their funds from old (non-quantum-resistant) outputs to those adhering to a quantum-resistant digital signature scheme. The transition protocol functions even if ECDSA has already been compromised. While our scheme requires modifications to the Bitcoin protocol, these can be implemented as a soft fork.

Bitcoin UTXOs value analysisThis is a jupyter notebook used to extract the total number of Bitcoins that are protected by revealed public keys using the BlockSci tool. We run this notebook on a r4.2xlarge EC2 Amazon instance using the AMI (ami-0650807b) that BlockSci offers. BlockSci and the blockchain data are already provisioned there. For more details on using BlockSci or in case you wish to use this in another environment, please follow the instructions found on their github page: https://github.com/citp/BlockScirevealed UTXOS.ipynb

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Keywords

blockchain, ECDSA, quantum resistance, bitcoin, Quantum computing, quantum computing

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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