Paper Info
Title
Parametric And Adaptive Encryption Of Feature-Based Computer-Aided Design Models For Cloud-Based Collaboration
Abstract
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.
Year
DOI
Venue
2017
10.3233/ICA-160535
INTEGRATED COMPUTER-AIDED ENGINEERING
Keywords
Field
DocType
Encryption, design feature, cloud, collaboration
Data mining,Computer science,Computer Aided Design,Encryption,Parametric statistics,Artificial intelligence,Feature based,Multimedia,Machine learning,Cloud computing
Journal
Volume
Issue
ISSN
24
2
1069-2509
Citations 
PageRank 
References 
2
0.50
20
Authors
5
Name
Order
Citations
PageRank
X. T. Cai1333.06
Sheng Wang292.70
Xin Lu352.30
W. D. Li437434.17
Y. W. Liang520.50