Mirhosseini, Mehrnaz/MM (2012) Modeling and analysis of secure collaborative design via function-parameter matrix. Masters thesis, Concordia University.
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Abstract
In order to keep the competitive advantages in today’s global business market, it is critical for the companies to establish an effective engineering collaboration and protect the intellectual properties of the original manufacturer companies. The purpose of this thesis is to develop the modularization method introduced by Li (2007), in order to protect intellectual property from being shared by suppliers or other manufacturers that attempt to do reverse engineering in collaborative design. This thesis uses this introduced Function-Parameter matrix which represents the dependency relationships between shared and protected (IP) design parameters, in order to group and isolate all the intellectual property information. This modularization includes three phases of clustering that matches hierarchical clustering rules. Due to this clustering, tracking the protected parameters via inferences and selecting one set of low risk parameters for sharing with suppliers will be more effective while this method mitigates the risk of information leakage for intellectual property. Based on a matrix-based modular structure, two formulations are proposed to estimate the leakage risk of protected parameters due to the disclosure of shared parameters to suppliers and potential inferences. At the end, the DC motor and the relief valve system are used to examine the proposed modularization and measurement methods.
Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering |
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Item Type: | Thesis (Masters) |
Authors: | Mirhosseini, Mehrnaz/MM |
Institution: | Concordia University |
Degree Name: | M. Sc. |
Program: | Quality Systems Engineering |
Date: | 7 December 2012 |
Thesis Supervisor(s): | Li, Simon/SL |
Keywords: | Collaborative design, intellectual property, cluster analysis, modularization, information leakage, dependency matrix, hierarchical clustering, parametric design |
ID Code: | 974991 |
Deposited By: | MEHRNAZ MIRHOSSEINI |
Deposited On: | 19 Jun 2013 20:09 |
Last Modified: | 18 Jan 2018 17:39 |
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