State of the Art in Similarity Preserving Hashing Functions
نویسندگان
چکیده
One of the goals of digital forensics is to analyse the content of digital devices by reducing its size and complexity. Similarity preserving hashing functions help to accomplish that mission through a resemblance comparison between different files. Some of the best-known functions of this type are the context-triggered piecewise hashing functions, which create a signature formed by several hashes of the initial file. In this contribution, we present the state of the art of the most important similarity preserving hashing functions, analysing their main features. We conclude our work listing the most relevant properties that such type of functions should satisfy in order to improve their efficiency.
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