Username   Password       Forgot your password?  Forgot your username? 


Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006


A Visual Cryptography Scheme-Based DNA Microarrays

Volume 14, Number 2, February 2018, pp. 334-340
DOI: 10.23940/ijpe.18.02.p14.334340

Xuncai Zhang, Zheng Zhou, Yangyang Jiao, Ying Niu, Yanfeng Wang

School of Electrics and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China


Visual cryptography is a cryptographic technique that allows visual information to be encrypted in such a way that the decryption can be performed by humans. The power of DNA molecule comes from its memory capacity and parallel processing. In this article, a visual encryption algorithm based on DNA microarrays is proposed, which successfully integrates the advantages of the algorithm in information security with the natural advantages of modern biotechnology. The algorithm converts plaintext into QR code and then uses the visual encryption scheme to encrypt the QR code image. It combines with DNA microarray technology to achieve information encryption and decryption. Security analysis shows that this algorithm has high security.


References: 24

    1. G. Ateniese, C. Blundo, A. D. Santis, and D. R. Stinson, “Visual Cryptography for General Access Structures,” Information and Computation, vol. 129, no.2, pp. 86-106, 1996
    2. G. Ateniese, C. Blundo, A. D. Santis, and D. R. Stinson, “Extended Capabilities for Visual Cryptography,” Theoretical Computer Science, vol. 250, no. 1-2, pp. 143–161, 2001
    3. Y. C. Chen, “Fully Incrementing Visual Cryptography from a Succinct Non-monotonic Structure,” IEEE Transactions on Information Forensics & Security, vol. 12, no. 5, pp. 1082-1091, 2017
    4. L. Chiu and K.H. Lee, “User-friendly Threshold Visual Cryptography with Complementary Cover Images,” Signal Processing, vol. 108, no. 3, pp. 476-488, 2015.
    5. S. Cimato and C. N. Yang, “Visual Cryptography and Secret Image Sharing,” CRC Press, Inc., Taylor &Francis, 2011
    6. M. Gnanaguruparan and S. Kak, “Recursive Hiding of Secrets in Visual Cryptography,” Cryptologia, vol. 26, no. 1, pp. 68-76, 2002
    7. M. Hirabayashi, H. Kojima, and K. Oiwa, “Design of True Random One-Time Pads in DNA XOR Cryptosystem,” Natural Computing, vol. 24, no. 3, pp. 174-183, 2010
    8. Y. C. Hou, S. C. Wei, and C. Y. Lin, “Random-grid-based Visual Cryptography Schemes,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 5, pp. 733-744, 2014
    9. K. M. Kurian, C. J. Watson, and A. H. Wyllie, “DNA Chip Technology,” The Journal of Pathology, vol.187, no. 3, pp. 267-271, 1999
    10. Q. Liu, L Wang, A. G. Frutos, A. E. Condon, R. M. Corn, and L. M Smith, “DNA Computing on Surfaces,” Nature, vol. 403, no. 6766, pp. 175-179, 2000
    11. R. Lakshmanan and S. Arumugam. “Construction of a (k, n)-Visual Cryptography Scheme,” Designs Codes & Cryptography, vol. 82, no. 3, pp. 629-645, 2017
    12. G. C. Le Goff, L. J. Blum, and C. A. Marquette, “Shrinking Hydrogel‐DNA Spots Generates 3D Microdots Arrays,” Macromolecular Bioscience, vol.13, no. 2, pp. 227-233, 2013
    13. T. Livache, B. Fouque, A. Roget, J. Marchand, G. Bidan, R. Téoule, and G. Mathis, “Polypyrrole DNA Chip on a Silicon Device: Example of Hepatitis C Virus Genotyping,” Analytical Biochemistry, vol. 255, no. 2, pp. 188-194, 1998
    14. M. X. Lu, X. J. Lai, G. Z. Xiao, and L. Qin, “A Symmetric Encryption Method based on DNA Technology,” Science in China (Series E), vol.37, no. 2, pp. 175-182.2007
    15. M. Naor and A. Shamir, “Visual Cryptography,” Lecture Notes in Computer Science, vol. 950, no. 9, pp. 1-12, Apr. 1995
    16. M. Ogihara and A. Ray, “DNA Computing on a Chip,” Nature, vol.403, no.6766, pp. 143-144, 2000
    17. F. Praetorius, and H. Dietz, “Self-assembly of Genetically Encoded DNA-protein Hybrid Nanoscale Shapes,” Science, vol. 355, no. 6331, eaam5488, 2017
    18. S. Pramanik and S. K. Setua, “DNA Cryptography,” IEEE International Conference on Electrical & Computer Engineering, vol. 90, no. 1, pp. 551-554, 2013
    19. R. D. Prisco and A. D. Santis, “On the Relation of Random Grid and Deterministic Visual Cryptography,” IEEE Transactions on Information Forensics & Security, vol.9, no. 4, pp. 653-665, 2017
    20. E. Rasul, H. A. Abdul, and F.I. Ismail, “Chaos-based Image Encryption Using a Hybrid Genetic Algorithm and a DNA Sequence,” Optics and Lasers in Engineering, vol. 56, no. 5, pp. 83-93, 2014
    21. J. H. Reif and H. John, “Scaling Up DNA Computation,” Science, vol.332, no. 6034, pp. 1156-1157, 2011
    22. E. R. Verheul and H. C. A. Van Tilborg, “Constructions and Properties of k Out of n Visual Secret Sharing Schemes,” Design Codes and Cryptography, vol. 11, no. 2, pp. 179-196, 1997
    23. G. Wang, W. Yan, and M. Kankanhalli, “Content based Authentication of Visual Cryptography,” Multimedia Tools and Applications, vol. 76, no. 7, pp. 9427-9441, 2017.
    24. X. Zhang, F. Han, and Y. Niu, “Chaotic Image Encryption Algorithm Based on Bit Permutation and Dynamic DNA Encoding,” Computational Intelligence and Neuroscience, (online since August 20, 2017). (DOI 10.1155/2017/6919675)


      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

      Download this file (IJPE-2018-02-14.pdf)IJPE-2018-02-14.pdf[A Visual Cryptography Scheme-Based DNA Microarrays]454 Kb


      Prev Next

      Temporal Multiscale Consumption Strategies of Intermittent Energy based on Parallel Computing

      Huifen Chen, Yiming Zhang, Feng Yao, Zhice Yang, Fang Liu, Yi Liu, Zhiheng Li, and Jinggang Wang

      Read more

      Decision Tree Incremental Learning Algorithm Oriented Intelligence Data

      Hongbin Wang, Ci Chu, Xiaodong Xie, Nianbin Wang, and Jing Sun

      Read more

      Spark-based Ensemble Learning for Imbalanced Data Classification

      Jiaman Ding, Sichen Wang, Lianyin Jia, Jinguo You, and Ying Jiang

      Read more

      Classification Decision based on a Hybrid Method of Weighted kNN and Hyper-Sphere SVM

      Peng Chen, Guoyou Shi, Shuang Liu, Yuanqiang Zhang, and Denis Špelič

      Read more

      An Improved Algorithm based on Time Domain Network Evolution

      Guanghui Yan, Qingqing Ma, Yafei Wang, Yu Wu, and Dan Jin

      Read more

      Auto-Tuning for Solving Multi-Conditional MAD Model

      Feng Yao, Yi Liu, Huifen Chen, Chen Li, Zhonghua Lu, Jinggang Wang, Zhiheng Li, and Ningming Nie

      Read more

      Smart Mine Construction based on Knowledge Engineering and Internet of Things

      Xiaosan Ge, Shuai Su, Haiyang Yu, Gang Chen, and Xiaoping Lu

      Read more

      A Mining Model of Network Log Data based on Hadoop

      Yun Wu, Xin Ma, Guangqian Kong, Bin Wang, and Xinwei Niu

      Read more
      This site uses encryption for transmitting your passwords.