Robust Hashing for Models

Salvador Martínez (1) , Sébastien Gérard (2) , Jordi Cabot (3)

1 LISE - Laboratoire d'Ingénierie dirigée par les modèles pour les Systèmes Embarqués
2 LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies
3 ICREA - Institució Catalana de Recerca i Estudis Avançats = Catalan Institution for Research and Advanced Studies

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.

Mots clés: Robust hashing; Near-duplicate detection; Locality-sensitive hashing

Domaines: Informatique [cs]; Génie logiciel [cs.SE]

Salvador Martínez, Sébastien Gérard, Jordi Cabot. Robust Hashing for Models. 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, Oct 2018, Copenhagen, Denmark. pp.312-322, ⟨10.1145/3239372.3239405⟩. ⟨hal-02388808⟩