sFEA: A secure finite element analysis technique

[+] Author and Article Information
Siva Chaitanya Chaduvula

Graduate Research Assistant, School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907

Mikhail J. Atallah

Distinguished Professor, Computer Science, Purdue University, West Lafayette, Indiana 47907

Jitesh H. Panchal

Associate Professor School, of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907

1Corresponding author.

ASME doi:10.1115/1.4042695 History: Received September 14, 2018; Revised January 25, 2019


Designers need a way to overcome information-related risks, including information leakage and misuse by their own collaborators during collaborative product realization. Existing cryptographic techniques aimed at overcoming these information-related risks are computationally expensive and impractical even for moderate problem sizes, and legal approaches such as non-disclosure agreements are not effective. The computational practicality problem is particularly pronounced for computational techniques such as the finite element analysis (FEA). In this paper, we propose a technique that enables designers to perform simulations, such as FEA computations, without the need for revealing their information to anyone, including their design collaborators. We present a new approach, the secure Finite Element Analysis approach, which enables designers to perform FEA without having to reveal structural/material information to their counterparts even though the computed answer depends on all the collaborators' confidential information. We build sFEA using computationally efficient protocols implementing a secure co-design framework. One of our findings is that the direct implementation of using secure co-design framework for FEA (termed as Naive sFEA) suffers from lack of scalability. To overcome these limitations, we propose Hybrid sFEA that implements performance improvement strategies. The results indicate that the computational burden imposed by Hybrid sFEA makes it challenging for large-scale FEA - our scheme significantly increases the problem sizes that can be handled when compared to implementations using previous algorithms and protocols, but large enough problem sizes will swamp our scheme as well.

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