IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v537y2020ics0378437119315274.html
   My bibliography  Save this article

Public key digital contents confidentiality scheme based on quantum spin and finite state automation

Author

Listed:
  • Batool, Syeda Iram
  • Amin, Muhammad
  • Waseem, Hafiz Muhammad

Abstract

Numerous encryption plans are legitimately founded on the transformation of frameworks or by characterizing the strict guidelines. Most of the security systems are based on mathematical structures and their applications in diverse applied sciences. We propose here an advanced digital contents confidentiality scheme to simulate the phenomenon rather than creating rigid rules. We operate digital data trailed by quantum spin states for specific phase and finite state machine for limited number of rounds. The extent of the presented article revolves around the development and deployment of public key cryptosystem basis on the concepts of quantum spin states and finite state machine effectively. Both states (spin and finite state machine) provide the high degree of naturalness contrasted with ordinary cryptosystem.

Suggested Citation

  • Batool, Syeda Iram & Amin, Muhammad & Waseem, Hafiz Muhammad, 2020. "Public key digital contents confidentiality scheme based on quantum spin and finite state automation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
  • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119315274
    DOI: 10.1016/j.physa.2019.122677
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119315274
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2019.122677?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Feihu Xu & Juan Miguel Arrazola & Kejin Wei & Wenyuan Wang & Pablo Palacios-Avila & Chen Feng & Shihan Sajeed & Norbert Lütkenhaus & Hoi-Kwong Lo, 2015. "Experimental quantum fingerprinting with weak coherent pulses," Nature Communications, Nature, vol. 6(1), pages 1-9, December.
    2. Majid Khan & Hafiz Muhammad Waseem, 2018. "A novel image encryption scheme based on quantum dynamical spinning and rotations," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-23, November.
    3. Artur Ekert & Renato Renner, 2014. "The ultimate physical limits of privacy," Nature, Nature, vol. 507(7493), pages 443-447, March.
    4. Ben W. Reichardt & Falk Unger & Umesh Vazirani, 2013. "Classical command of quantum systems," Nature, Nature, vol. 496(7446), pages 456-460, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alghafis, Abdullah & Waseem, Hafiz Muhammad & Khan, Majid & Jamal, Sajjad Shaukat, 2020. "A hybrid cryptosystem for digital contents confidentiality based on rotation of quantum spin states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alghafis, Abdullah & Waseem, Hafiz Muhammad & Khan, Majid & Jamal, Sajjad Shaukat, 2020. "A hybrid cryptosystem for digital contents confidentiality based on rotation of quantum spin states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    2. Dorit Aharonov & Jordan Cotler & Xiao-Liang Qi, 2022. "Quantum algorithmic measurement," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    3. Arshad, Usman & Khan, Majid & Shaukat, Sajjad & Amin, Muhammad & Shah, Tariq, 2020. "An efficient image privacy scheme based on nonlinear chaotic system and linear canonical transformation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 546(C).
    4. Munir, Noor & Khan, Majid & Jamal, Sajjad Shaukat & Hazzazi, Mohammad Mazyad & Hussain, Iqtadar, 2021. "Cryptanalysis of hybrid secure image encryption based on Julia set fractals and three-dimensional Lorenz chaotic map," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 826-836.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119315274. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.