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Record W3199898834 · doi:10.1504/ijsn.2021.10041097

Impact of Post-Quantum Hybrid Certificates on PKI, Common Libraries, and Protocols

2021· article· en· W3199898834 on OpenAlexaff
Carlisle Adams, Mike Ounsworth, Serge Mister, Fabian Willems, John Edward Gray, Jafar Zahed, Jinnan Fan

Bibliographic record

VenueInternational Journal of Security and Networks · 2021
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsEntrust (Canada)University of Ottawa
Fundersnot available
KeywordsPublic key infrastructureComputer scienceRevocation listCertificate authorityCertificateComputer securityCryptographyPublic-key cryptographyCertificationPublic key certificateKey managementRevocationEncryptionOperating systemTheoretical computer scienceOverhead (engineering)

Abstract

fetched live from OpenAlex

In this work, we assessed the impact of post-quantum (PQ) cryptography on public key infrastructure (PKI). First, we modified a commercially available certification authority (CA) to issue 'hybrid' certificates (X.509 certificates with PQ extensions). Then we assessed the impact of using these certificates on some existing protocols, including TLS, OCSP, CMP, and EST, with open-source libraries OpenSSL and CFSSL, and with a commercially available cryptographic toolkit. We found that most of the protocols and libraries we tested worked with hybrid certificates, and some of the failures could be overcome with minor modifications to the existing software. Our work differentiates from and extends previous work by focusing on the impact of PQ algorithms on certificate issuance, revocation, and management protocols, which are necessary for enterprises to manage PKI in their environments. The impact on TLS is also investigated, allowing consistency with previous results to be evaluated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.089
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.089
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0040.011
Open science0.0030.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.294
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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