On Watermarking for Collaborative Model-Driven Engineering

• Published in: IEEE access Vol. 6; pp. 29715 - 29728
• Main Authors: Martinez, Salvador, Gerard, Sebastien, Cabot, Jordi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); IEEE Access
• Subjects: Adaptation models; Algorithms; Biological system modeling; Collaboration; Computer Science; Data mining; Data models; Domains; Engineering; Enginyeria; enginyeria dirigida per models; Error correction; Ingeniería; ingeniería dirigida por modelos; Intellectual property; intellectual property protection; Labeling; marcas de agua; marques d'aigua; Model driven engineering; Modeling and Simulation; protecció de la propietat intel·lectual; protección de la propiedad intelectual; Software Engineering; Watermarking
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2841020

Collaborative development scenarios often require models to be shared among the different stakeholders. These stakeholders are mostly remote with communication typically taking place over untrusted networks. This raises the need for effective intellectual property (IP) protection mechanisms for the shared models. Watermarking, an information hiding technique aimed at providing the means to verify the authenticity, integrity, and ownership of digital assets, has proved useful to provide IP protection in both media (images, audio, and video) and non-media domains (databases, XML documents, and graphs). In this paper, we explore the adaptation of the concept of watermarking to the modeling domain. We provide a novel and robust labeling mechanism based in the use of locality sensitive hashing and error correction codes. This labeling mechanism enables the integration of state-of-the-art watermarking algorithms in model-driven development approaches. Additionally, we leverage on the labeling mechanism to contribute a zero-watermarking algorithm to watermark models without introducing distortions to the data. We demonstrate the feasibility of our approach by providing a prototype implementation of our contribution.