IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

A Software-Based ASCON–QPP Security for Lightweight Metaverse Applications

A Software-Based ASCON–QPP Security for Lightweight Metaverse Applications
View Sample PDF
Author(s): Zeesha Mishra (NIIT University, India), Dhruvika Bansal (NIIT University, India)and Garvit Bajaj (NIIT University, India)
Copyright: 2027
Pages: 42
Source title: Next-Generation Security Frameworks for the Metaverse
Source Author(s)/Editor(s): Mishall Hammed Al-Zubaidie (University of Thi-Qar, Iraq & University of Southern Queensland, Australia)
DOI: 10.4018/979-8-2600-2313-6.ch004

Purchase

View A Software-Based ASCON–QPP Security for Lightweight Metaverse Applications on the publisher's website for pricing and purchasing information.

Abstract

The growing dependence on data-centric software systems demands strong protection of sensitive information, where confidentiality, integrity, and high-quality randomness are critical. Permutation-based cryptography provides structured diffusion and unpredictability for secure software environments. ASCON, selected by National Institute of Standards and Technology (NIST) as a lightweight cryptography standard, offers an efficient and modular permutation suitable for experimentation. The Quantum Permutation Pad (QPP) introduces highly nonlinear, entropy-rich byte-level permutations to enhance state mixing. This chapter integrates ASCON with QPP in software across baseline, 6-round, and 12-round configurations and evaluates performance using randomness tests, bit-distribution, avalanche effect, and key sensitivity. Results show avalanche improvements of 21.7% and 39.1%, a 0.25% entropy increase, 100% serial correlation reduction, ~4% bit-bias reduction, and only 1–1.5% throughput overhead.

Related Content

K. Muthamil Sudar. © 2027. 26 pages.
Indranil Saha, Anuva Aggarwal, Taher Aurangabadi, Zeesha Mishra. © 2027. 36 pages.
Qais Al-Na'amneh. © 2027. 24 pages.
Zeesha Mishra, Dhruvika Bansal, Garvit Bajaj. © 2027. 42 pages.
Amrutha Kolhar, Sridevi. © 2027. 32 pages.
Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Jaime Aguilar-Ortiz, Francisco R. Trejo-Macotela, Eric Simancas-Acevedo. © 2027. 38 pages.
Semila Fernandes, Anshul Dhunna. © 2027. 40 pages.
Body Bottom