How hardworking hashes enable the magic of blockchain

A primary objective for using blockchain is to allow two (or more) people to look independently at a data entry, at their own convenience, and know whether they are able to rely on  the authenticity of that data – or not. The reason that data authenticity is so critical is because the data entry, recorded in a block of a blockchain, might be a transfer of payment, a private message or any other piece of information users might wish to share.

Blockchain are validation protocols written to determine if transactions should be included or not, whatever those transactions may be.
So how does all of this work? – With hashing


What is a hash?
Hashes are used everywhere in the construction of blockchains.  So what does that mean exactly?

We can explain this process with Geeq Data, which is a blockchain-based data validation application. Here are some key points for the discussion

1: A hash is a method to compress a computer-readable data file.
2: Even a slight change in the data results in a new and very different hash

3: A hash is a snapshot of the data submitted at the time.

4:You cannot look at a hash, in isolation, and determine what the original data was.


Point 1 – A hash is a method to compress a computer-readable data file.

Consider the image of this  leopard lying in a 

tree. The image is represented as a .png fileytype and the file size is 534 kb.

The way to obtain a hash of a data file is to use a hashing function, which is a mathematical process. The hashing function needs an input in order to produce an output.

In this case, the image file is the input. Inputting this image file into a hashing function, is called “hashing it”, and it results in an output of fixed length.

Doing this task with Geeq Data, the process is as simple as uploading a file and clicking a button that says “Hash it”. The mathematical hashing happens in the background.

Data files can be very large, so the hash of the input file is usually much smaller than the original file itself.  In Geeq Data’s case, the original image file is 16,687.5 times larger than the hashed data.