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Hashing Module Level Design
Designing a Hash function that results in minimum size and no collisions Ask Question. Asked 7 years, 1 month ago. Active 7 years, 1 month ago. Viewed 2k times. Thanks a lot Daniel.
Designing a good non-cryptographic hash function
Daniel Daniel 8 3 3 bronze badges. You might find it useful to study examples like this in your research. If you are reading this, I can almost guarantee that you have some interface with a hashing algorithm right now! Please note I will be utilising the passlib package which contains over 30 password hashing algorithms, as well as a framework for managing existing password hashes.
What is Hashing?
First of all we must select a hashing algorithm to use, to help with this from the team at passlib they have provided a basic guideline of questions :. From this, we will use the argon2 hashing algorithm. As normal, it is best practice to set up a virtual environment or conda environment and install the dependencies, in this case passlib.
Following importing the hashing algorithm, to hash the password in our case is very simple and we can have a peak at what the output hash looks like:.
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While this seems as if it would make the algorithm easier to break, imagine a scenario where every password is hashed using an hashing algorithm with randomised parameters; verifying passwords would be a nightmare. If we run this again, we can check that the outputs are completely different due to the randomly generated salt. To do this with passlib, it is as simply as calling the.
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Our password verification system works, now we would like to check that if the user inputs a incorrect password that our algorithm returns correctly false. Hopefully this has given you some insight into what hashing algorithms are, how they are used and how to use them with Python.
Low power and pipelined secure hashing algorithm-3(SHA-3) - IEEE Conference Publication
For some weak er hashes there may be shortcuts , besides brute forcing. I talked about this in point 1 a little. The design of a hash may or may not be complex but in all cases the algorithms are out in the open. No secrets there. Any algorithm that relies on keeping the actual algorithm secret will eventually and usually sooner than later fail. See Kerckhoffs's principle : "A cryptosystem should be secure even if everything about the system, except the key, is public knowledge" or Shannon's maxim.
A lot of hashes, at some point or another, rely on a modulo operation. Here's an intro to one-way hash functions for some more in-depth detail than this post will provide archived version here , just in case. The algorithm, software program or library is or should be already in the open. There are hashes that were fine in the past but turned out to be flawed MD5 for example, is considered ' broken '.
Besides 'hackers' there's a bunch of people called Cryptanalysist who's job it is or who enjoy looking for weaknesses or flaws in cryptographic algorithms. It's safe to assume that, over time, more algorithms will follow MD5. How many, and at which rate, is another matter. There's a lot of widely known and even lesser known hashes. You can read more about attacks here. No, you cannot back-calculate the original data from a hash, for the very simple reason that there's an infinity of inputs that can have the same hash.
So what you could do is find some input that has the same hash output a collision. As others have explained, doing this by brute force is quite hard, and in the end, you may end up with an input that has no relationship whatsoever with the "original" input. But that input is meaningless. So even though you found a collision which, again, is quite difficult , that collision is quite useless. Finding another input which has the same hash and makes sense and provides an advantage to the attacker is quite a lot more difficult. Unless there is a flaw in the hash function, of course.
There are some excellent answers here to the OP's actual questions so I wont go over old ground, but there is one aspect about hashes that has not really been touched on that might improve the OPs understanding of hashes and what they are good for. By definition, a hash function maps data of arbitrary size to data of fixed size.
In other words, the hash value tells you practically nothing about the input value used to generate it, not even its length, making reverse engineering effectively impossible. Yes it is possible to produce input data that produces the same hash value, but it is not possible to take a hash value and work out what the input data that produced it was, which makes hashes very attractive to cryptographers.
An example of an extremely simple but very commonly used hash is the parity bit; this is set true 1 or false 0 to indicate whether the number of bits set in the input is even or odd. Change one bit in the input and you get a different parity - extremely useful for a quick corruption check! It doesn't matter if the input is the complete works of Shakespeare or a number between one and ten.
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Of course, if you change an even number of bits, then the parity doesn't change you have a collision! Hashes ensure uniqueness , when they are secure and wide-enough. That's much more certain than closing a tap ensures the water does not run, when the tap is not broken and of the appropriate model. That's because then we know no feasible method to find two distinct inputs that yield the same hash; even though we know, by the pigeonhole principle, that there are such inputs.
The practitioner applies that, using a bit hash giving bit security like SHA to guard against current attacks; or a bit hash giving bit security like SHA to guard against any foreseeable progress, including hypothetical quantum computer. In such setups, collision do not happen.
We should rather fear penetration of the computer, global warming, and comet impact. Assuming a secure and wide-enough hash as above, the only known strategy to find the original data is to try possible values of that original data, hashing them, until finding one which hash matches. It is then practically certain that the original data was found proof: if it was not, knowledge of the original data would allow to exhibit two different values with the same hash, and we can't!
For example, given the SHA hash of a digit Credit Card number, it is possible to find that number with at most 10 15 hashes the last digit is a Luhn check digit. If on the other hand what's hashed is a digit Credit Card number followed by bits or 43 base characters uniformly random and unknown to the attacker , her or his tasks is hopeless. Hashes, as you have observed, are not guaranteed to be unique for every input - they can't be, as they are of finite size, with potentially infinite inputs.
They only "group" inputs into outputs in the context of using hashes in data structures, this is sometimes referred to as a bucket. This is still useful. By way of example, a very simple hashing function might be "given a non-empty, single-byte-character string input, use the first byte as the hash". Consider the properties of such a function:.
Even a very simple hashing function like this might be useful for some purposes very simple dictionary data structures perhaps - a comparison between two inputs can check their hashes, and trivially reject the possibility that they are the same times out of This could make a data structure using it quite a lot faster. But, if two inputs resulted in the same hash known as a "collision" , the whole of each input would need to be compared, to see whether they are really the same or not.
Of course, such a simple hash function would be useless for hashing something like a password and pretty poor for data structures too. A good hash function would have additional properties:. And depending on the purpose to which the hash will be put, there will be other considerations too:.