Name
Abstract | ||
|---|---|---|
The design of hash functions by means of evolutionary computation is a relatively new and unexplored problem. In this work, we use Genetic Programming (GP) to evolve robust and fast hash functions. We use a fitness function based on a non-linearity measure, producing evolved hashes with a good degree of Avalanche Effect. Efficiency is assured by using only very fast operators (both in hardware and software) and by limiting the number of nodes. Using this approach, we have created a new hash function, which we call gp-hash, that is able to outperform a set of five human-generated, widely-used hash functions. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1143997.1144300 | GECCO |
Keywords | Field | DocType |
fast operator,genetic programming,hash function,unexplored problem,evolutionary computation,non-linearity measure,good degree,widely-used hash function,new hash function,avalanche effect,hash functions,fitness function | SHA-2,Hash tree,Double hashing,Computer science,Collision resistance,Hash buster,Theoretical computer science,Merkle tree,Hash function,Hash chain | Conference |
ISBN | Citations | PageRank |
1-59593-186-4 | 7 | 0.94 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| César Estébanez | 1 | 65 | 7.39 |
| Julio César Hernández Castro | 2 | 189 | 37.03 |
| Arturo Ribagorda | 3 | 669 | 50.25 |
| Pedro Isasi | 4 | 370 | 42.14 |