Interpolating Spline Curve-Based Perceptual Encryption for 3D Printing Models
AbstractWith the development of 3D printing technology, 3D printing has recently been applied to many areas of life including healthcare and the automotive industry. Due to the benefit of 3D printing, 3D printing models are often attacked by hackers and distributed without agreement from the original providers. Furthermore, certain special models and anti-weapon models in 3D printing must be protected against unauthorized users. Therefore, in order to prevent attacks and illegal copying and to ensure that all access is authorized, 3D printing models should be encrypted before being transmitted and stored. A novel perceptual encryption algorithm for 3D printing models for secure storage and transmission is presented in this paper. A facet of 3D printing model is extracted to interpolate a spline curve of degree 2 in three-dimensional space that is determined by three control points, the curvature coefficients of degree 2, and an interpolating vector. Three control points, the curvature coefficients, and interpolating vector of the spline curve of degree 2 are encrypted by a secret key. The encrypted features of the spline curve are then used to obtain the encrypted 3D printing model by inverse interpolation and geometric distortion. The results of experiments and evaluations prove that the entire 3D triangle model is altered and deformed after the perceptual encryption process. The proposed algorithm is responsive to the various formats of 3D printing models. The results of the perceptual encryption process is superior to those of previous methods. The proposed algorithm also provides a better method and more security than previous methods. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Pham, G.N.; Lee, S.-H.; Kwon, K.-R. Interpolating Spline Curve-Based Perceptual Encryption for 3D Printing Models. Appl. Sci. 2018, 8, 242.
Pham GN, Lee S-H, Kwon K-R. Interpolating Spline Curve-Based Perceptual Encryption for 3D Printing Models. Applied Sciences. 2018; 8(2):242.Chicago/Turabian Style
Pham, Giao N.; Lee, Suk-Hwan; Kwon, Ki-Ryong. 2018. "Interpolating Spline Curve-Based Perceptual Encryption for 3D Printing Models." Appl. Sci. 8, no. 2: 242.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.