Entropy measures the “chaos” of a randomness source. The timestamps and transactions in Bitcoin pack a sustainably high entropy source. In this case, the Bitcoin blockchain is used to produce random numbers. Thus, for high-stake randomness-based applications, this method is not a secure one. When the reward of the lottery is small, the miners have little motivation to tamper with the block but as soon as this amount is larger than the block reward plus the transaction fees, there is a chance that miners will start influencing the block-hash to generate their desired numbers. However, this hash is subject to manipulation by the miners guarding the blockchain. As we can see, at 7 PM, no one can predict the hash of the block at 8 PM which makes the service a sound one. This ticket is calculated based on the hash of the first block accessible for everyone on the Bitcoin blockchain after 8 PM. After 7 PM, the buying ticket phase is closed, the protocol proceeds to the next phase which is to determine the winning numbers for a ticket. The players first buy a ticket by placing their number before a specific time, say 7 PM every day. A block, once added to the blockchain, is likely to stay there forever, therefore, everyone can verify the correctness of the generated numbers.Ĭonsider an example of a lottery service that adopts this method. As the hash is deterministic, everyone will get the same result. In the Block-hash approach, the hash of blocks or transactions is used as the source of randomness. We will briefly discuss some of these solutions to clarify the current state of blockchain-based random number generation: 1. As a result, many attempts have been made to construct a blockchain-based random number generator. Since then, the phrase “ blockchain random number” has been getting more and more attention from computer scientists. All this changed in 2009 with the invention of blockchain technology. Traditional RNGs cannot resolve this since they are centralized (this article explains in-depth the terms centralization, and decentralization) and running solely on a local source and or under the control of a group of individuals or organizations. Many applications (such as those mentioned in this article) today not only require randomness in the unpredictability manner but also require public randomness which cannot be manipulated or in control of any organization. It is widely agreed that these sources behave in an inherently unpredictable and non-deterministic manner. TRNGs, on the other hand, derive randomness from various physical phenomena such as thermal noise, radio noise and so on. These RNGs are based on algorithms implemented on finite-state machines to produce pseudo-random determinism sequences from initial values called seeds in mathematical processes. PRNGs utilize mathematical algorithms, which are considered deterministic. Traditionally, RNGs can be classified into two types: Pseudo-Random Number Generators (PRNGs) and True Random Number Generators (TRNGs). Obtaining a true randomness source is always significantly challenging and a lot of effort has been devoted to constructing a good Random Number Generator (RNG). Randomness is an essential building block of many aspects of life ranging from theory such as security, cryptography, or computer simulation to forms of gambling like lotteries.
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