A new edit distance for fuzzy hashing applications.

Similarity preserving hashing applications, also known as fuzzy hashing functions, help to analyse the content of digital devices by performing a resemblance comparison between different files. In practice, the similarity matching procedure is a two-step process, where first a signature associated to the files under comparison is generated, and then a comparison of the signatures themselves is performed. Even though ssdeep is the best-known application in this field, the edit distance algorithm that ssdeep uses for performing the signature comparison is not well-suited for certain scenarios. In this contribution we present a new edit distance algorithm that better reflects the similarity of two strings, and that can be used by fuzzy hashing applications in order to improve their results.