Parametric and adaptive encryption of feature-based computer-aided design models for cloud-based collaboration

When a Computer Aided Design (CAD) model is shared in cloud for collaboration, it is a challenge to encrypt the confidential design features of the model for effective knowledge protection. This paper presents a novel encryption approach, which is based on geometric transformation encryption mechanisms on feature-based CAD models in supporting cloud-enabled collaboration. The innovation of the approach is centered on an Enhanced Encryption Transformation Matrix (EETM), which is characterized parametric, randomized and self-adaptive for feature encryption. Controllable parameters for transforming features in terms of zoom and deformation are defined in the EETM to facilitate users to conduct encryption transformation flexibly. A random probability mechanism is embedded into the parameters of the EETM in order to guarantee the security of the encrypted model. Furthermore, the parameters in the EETM are further enhanced to be self-adaptive to ensure the geometric validity of the encrypted model. The approach has been validated via a number of complex models to demonstrate the applicability and effectiveness for industrial applications.