Resilience Optimization of Post-Quantum Cryptography Key Encapsulation Algorithms.

BIKE asymmetric cryptography classic McEliece cryptography key encapsulation mechanism post-quantum cryptography

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
06 Jun 2023
Historique:
received: 07 05 2023
revised: 03 06 2023
accepted: 04 06 2023
medline: 10 7 2023
pubmed: 8 7 2023
entrez: 8 7 2023
Statut: epublish

Résumé

Recent developments in quantum computing have shed light on the shortcomings of the conventional public cryptosystem. Even while Shor's algorithm cannot yet be implemented on quantum computers, it indicates that asymmetric key encryption will not be practicable or secure in the near future. The National Institute of Standards and Technology (NIST) has started looking for a post-quantum encryption algorithm that is resistant to the development of future quantum computers as a response to this security concern. The current focus is on standardizing asymmetric cryptography that should be impenetrable by a quantum computer. This has become increasingly important in recent years. Currently, the process of standardizing asymmetric cryptography is coming very close to being finished. This study evaluated the performance of two post-quantum cryptography (PQC) algorithms, both of which were selected as NIST fourth-round finalists. The research assessed the key generation, encapsulation, and decapsulation operations, providing insights into their efficiency and suitability for real-world applications. Further research and standardization efforts are required to enable secure and efficient post-quantum encryption. When selecting appropriate post-quantum encryption algorithms for specific applications, factors such as security levels, performance requirements, key sizes, and platform compatibility should be taken into account. This paper provides helpful insight for post-quantum cryptography researchers and practitioners, assisting in the decision-making process for selecting appropriate algorithms to protect confidential data in the age of quantum computing.

Identifiants

pubmed: 37420546
pii: s23125379
doi: 10.3390/s23125379
pmc: PMC10303738
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : the European University of Atlantics
ID : N/A

Références

Nature. 2019 Oct;574(7779):505-510
pubmed: 31645734

Auteurs

Sana Farooq (S)

Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan.

Ayesha Altaf (A)

Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan.

Faiza Iqbal (F)

Department of Computer Science, University of Engineering & Technology (UET), Lahore 54890, Pakistan.

Ernesto Bautista Thompson (EB)

Higher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain.
Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico.
Project Management, Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA.

Debora Libertad Ramírez Vargas (DLR)

Higher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain.
Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico.
Universidade Internacional do Cuanza, Estrada Nacional 250, Bairro Kaluapanda, Cuito EN250, Angola.

Isabel de la Torre Díez (IT)

Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain.

Imran Ashraf (I)

Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages
1.00
Humans Magnetic Resonance Imaging Brain Infant, Newborn Infant, Premature
Humans Algorithms Software Artificial Intelligence Computer Simulation

Unsupervised learning for real-time and continuous gait phase detection.

Dollaporn Anopas, Yodchanan Wongsawat, Jetsada Arnin
1.00
Humans Gait Neural Networks, Computer Unsupervised Machine Learning Walking

Classifications MeSH