BlockChain News
Ergo Platform : “ Revisiting Difficulty Control for Blockchain Systems ”
as an attack .
One of these attacks , coin-hopping , is described in a recent paper dubbed “ Revisiting Difficulty Control for Blockchain Systems ”. The author , who coined the term “ coin-hopping ”, describes the fundamental flaw that makes this attack vector possible and how it causes a negative impact on the health of the Bitcoin network and , consequently , on its end users .
Since its inception , Bitcoin mining has gone from being a “ garage project ” to a competitive industrialized practice . According to the “ Global Cryptocurrency Benchmarking Study ”, a research paper recently published by the Cambridge Centre for Alternative Finance ( CCAF ), electricity consumption for Bitcoin mining facilities can be estimated at around 462MW . In this competitive environment , miners are expected to take every advantage they can get their hands on . This can range from ways to cut power costs , acquiring faster hash rate , and more . However , there are flaws in the Bitcoin network that have allowed for certain miners to gain an unfair advantage at the expense of the well-being of the network itself , which should not be considered as an incentivized advantage anymore , but rather
The paper , which can be read here , also provides a solution for this problem , which is to be implemented in the upcoming Ergo Platform , a project that aims to provide solutions for various cryptocurrency problems .
So , what is the problem , exactly ? The Bitcoin network has a fixed issuance rate which means that when new miners join the network , this issuance rate doesn ’ t increase . Instead , the “ mining difficulty ” changes , ensuring that the aforementioned issuance rate remains somewhat stable .
However , the current readjustment algorithm in Bitcoin assumes that the network hashing power doesn ’ t change drastically between the difficulty retargeting periods ( 2016 blocks ) but the paper demonstrates that due to the “ continuous non-linear growth ” of computational power , delay
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