Robust Hashing for Models

TL;DR: This work presents a novel robust hashing mechanism for models based on the use of model fragmentation and locality sensitive hashing and discusses the usefulness of this technique on a number of scenarios and its feasibility by providing a prototype implementation and corresponding experimental evaluation.

Abstract: The increased adoption of model-driven engineering (MDE) in complex industrial environments highlights the value of a company's modeling artefacts. As such, any MDE ecosystem must provide mechanisms to both, protect, and take full advantage of these valuable assets. In this sense, we explore the adaptation of the Robust Hashing technique to the MDE domain. Indeed, robust hashing algorithms (i.e. hashing algorithms that generate similar outputs from similar input data), have been proved useful as a key building block in intellectual property protection, authenticity assessment and fast comparison and retrieval solutions for different application domains. We present a novel robust hashing mechanism for models based on the use of model fragmentation and locality sensitive hashing. We discuss the usefulness of this technique on a number of scenarios and its feasibility by providing a prototype implementation and corresponding experimental evaluation.