Process Hashing In Crypto Sphere And Its Peculiarities
Hashing techniques are very popular in cryptocurrency field. Starting from the Bitcoin proposal in 2009, many projects have implemented different hashing algorithms in their solutions. Hashing is not new in the IT or mathematical areas. The history of hashing counts several different families with tenths of particular representatives along with some standalone proposals.
Commonly, hashing technique is used when it is necessary to get some short and verifiable data signature. Arbitrary data of different size is used as an input to a hash function. An output, however, will always be of the same size regardless of the input. Let’s examine hashing properties in detail.
Main Hashing Properties
There are many specific hashing algorithms with different particular data processing details. These differences influence their general properties. Among widespread hashing families, there are SHA, MD5, and BLAKE. Currently, developers propose new algorithms to increase security and strengthen efficiency. However, any hash function must correspond to several obligatory requirements.
It is very important for hashing to be predictable. This means that the input of a hash must always give the same output. If this is not true, the function cannot be used whatsoever. The main reason for such a requirement is that hashing as a reliable process is used to support data confirmation. You cannot confirm data if its hash may not be the same.
Hashing must be performed in a heartbeat. The main reason for this is that if data verification is slow, its preferability is decreased. Basically, there is not a single reason to make this procedure time consuming. We have examples of long preparation events that might take more than a day to be performed. This is not the case.
Hash Must Be Irreversible
This property is very interesting. Hashes are used for data processing according to the algorithm, and thus, in theory, they might be reverted. However, by using a complex hash structure and commands set, we can avoid data reversion and conceal it from malicious participants. To make things more perfect, the only way to check if a hash corresponds to the data is to calculate the hash again.
Avalanche Hashing Effect
Hash function is very complex but it will lose its reliability if small changes in input data result in small changes in output data. Thus, any proper hash function must have so-called avalanche effect, when changes, made even in a single bit, can influence the whole output. This should not be taken to the point of absurdity though. If any minor change influences all bits in the output, the hash is also considered weak.
Infeasible Hash Result Duplication
One important issue must be taken into account when in hashing big data amounts. Since a particular data set is much bigger than the output, it is possible to receive the same hash from different inputs. This problem is unavoidable but it should be delayed. As a rule, output values have a sufficient range to stay away from interception, yet this issue must be taken into account.
As we can see, hashing has several main properties. These properties are crucial because hashing is actively used in cryptography, data protection and verification, as well as when transferring money to other users. Nevertheless, hashing properties are strongly interconnected, and by taking into consideration one of them, we can positively influence the others.