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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2018 Vol.8(2): 186-190 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.2.685

3D Printing Model Random Encryption Based on Geometric Transformation

Ngoc-Giao Pham, Suk-Hwan Lee, Oh-Heum Kwon and Ki-Ryong Kwon
Abstract—Due to the fact that 3D printing has been recently applied in many areas of life, a large amount of 3D printing models have been attacked and stolen by hackers. Moreover, some special models and anti-weapon models used in 3D printing must be secured from unauthorized users. Therefore, 3D printing models must be encrypted before being stored and transmitted in order to prevent illegal copying. In this paper, we present a random encryption algorithm for 3D printing models. The proposed algorithm is based on randomly encrypting the vertices of each facet using a secret key after the geometric transformation process. Each facet of the 3D printing model is distorted by a geometric transformation and the three vertices of each distorted facet are then used to construct a 3 × 3 matrix. The coefficients of the constructed matrix are randomly encrypted using the random numbers of another matrix in order to generate the encrypted 3D printing model. The experimental results verify that the proposed algorithm is very effective for 3D printing models. The entire 3D triangle mesh is altered after the encryption process. The proposed algorithm is a better method and offers more security than the previously reported methods.

Index Terms—3D printing data, 3D printing security, 3D triangle mesh, geometric transformation and randomization.

Ngoc-Giao Pham, Oh-Heum Kwon, and Ki-Ryong Kwon are with the Dept. of IT Convergence and Application Engineering, Pukyong National University, Busan, South Korea (Corresponding Author: Ki-Ryong Kwon; e-mail: ngocgiaofet@gmail.com, ohkwn@pknu.ac.kr, krkwon@pknu.ac.kr).
Suk-Hwan Lee is with Dept. of Information Security, Tongmyong University, Busan, South Korea (e-mail: skylee@tu.ac.kr).


Cite: Ngoc-Giao Pham, Suk-Hwan Lee, Oh-Heum Kwon and Ki-Ryong Kwon, "3D Printing Model Random Encryption Based on Geometric Transformation," International Journal of Machine Learning and Computing vol. 8, no. 2, pp. 186-190, 2018.

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