Results for functional_hash

tools and context for hashing bytes

bloom filter is a probabilistic filter which SPV clients provide so we can filter the transactions we send them -- this allows for significantly more efficient transaction and block downloads

simple HD chain data model

B-field datastructure implementation in Rust

Although designed for use as a hash function to ensure security, SipHash is fundamentally different from cryptographic hash functions like SHA in that it is only suitable as a message authentication code: a keyed hash function like HMAC. That is, SHA is designed so that it is difficult for an attacker to find two messages X and Y such that SHA(X) = SHA(Y), even though anyone may compute SHA(X). SipHash instead guarantees that, having seen Xi and SipHash(Xi, k), an attacker who does not know the key k cannot find (any information about) k or SipHash(Y, k) for any message Y ∉ {Xi} which they have not seen before. (from wikipedia)

secure hash algorithm 1

This library computes the pedersen hash of given inputs and modular arithmetic.

A minimal Bloom Filter implementation in Rust

SHA-256 and SHA-512 are novel hash functions computed with eight 32-bit and 64-bit words, respectively. They use different shift amounts and additive constants, but their structures are otherwise virtually identical, differing only in the number of rounds. (from wikipedia)

Miden Cryptographic primitives

SHA-256 and SHA-512 are novel hash functions computed with eight 32-bit and 64-bit words, respectively. They use different shift amounts and additive constants, but their structures are otherwise virtually identical, differing only in the number of rounds. (from wikipedia)

Although part of the same series of standards, SHA-3 (Keccak) is internally different from the MD5-like structure of SHA-1 and SHA-2. -- Keccak is based on a novel approach called sponge construction. Sponge construction is based on a wide random function or random permutation, and allows inputting (`absorbing` in sponge terminology) any amount of data, and outputting (`squeezing`) any amount of data, while acting as a pseudorandom function with regard to all previous inputs. This leads to great flexibility. (from wikipedia)