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Record W3103656886 · doi:10.22215/etd/2020-14317

Error Correcting Codes in Post-Quantum Cryptography

2020· dissertation· en· W3103656886 on OpenAlexaff
John-Marc Desmarais

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicCoding theory and cryptography
Canadian institutionsCarleton University
Fundersnot available
KeywordsMcEliece cryptosystemCryptosystemComputer scienceKey encapsulationCryptographyPublic-key cryptographyCoding theoryTheoretical computer scienceNISTError detection and correctionKey sizeComputer securityAlgorithmSymmetric-key algorithmEncryption

Abstract

fetched live from OpenAlex

This thesis gives an overview of the currently most mature key encapsulation mechanisms (KEMs) based on the theory of error correcting codes.It includes an introduction to the theory of error correcting codes in so much as it applies to these systems and how it can be used to encapsulate keys through a public key (PK) cryptosystem.In order to add context to the KEMs, first the required basics of coding theory and a selection of some of the most common error correcting codes are covered.Then, we revisit public key cryptosystems, key encapsulation, and the security threat models that are being used.This is followed by a thorough description of the current NIST candidates for KEM using post-quantum cryptography: Classic McEliece, BIKE, LEDAcrypt, and HQC.We do not include rank metric methods such as ROLLO and RQC, which were NIST candidates until the second round, since they involve different features than those studied in this thesis.The thesis is intended as a survey of current methods being used in this field.We also establish some of the problems which may pose interesting for further research.iii In 1994, Shor [62] came up with a few algorithms for quantum computers that made the previously computationally infeasible problems of factoring large numbers and finding discrete logarithms of numbers in modular rings suddenly feasible.In the subsequent decades, this did not have much effect on the popularity of RSA as a standard for asymmetric encryption.But, now in the advent of quantum computers of increasing bit sizes, this problem has returned.At the same time, the large key sizes required by code-based systems are no longer as much of an issue due to increasing access to high speed networks.In 2016, the National Institute of Standards and Technology (NIST) published a call for proposals for post-quantum cryptosystems that would not be based on the hardness of factoring large numbers or of finding discrete logarithms.This thesis provides a thorough description of the Round 2 Candidates of this competition which are based upon coding theory.These are Classic McEliece, BIKE, LEDAcrypt, and HQC. Thesis StructureChapter 2 covers coding theory as is necessary to understand the current state of the art in code-based cryptography.It begins with a description of the theory of error correcting codes and then describes several of the most popular code systems currently in use including Hamming Codes, BCH codes, Low-Density Parity-Check (LDPC) codes, Medium-Density Parity-Check (MDPC) codes, and Quasi-cyclic (QC) codes.Chapter 3 introduces public key cryptosystems and how coding theory fits into this paradigm.It then presents the leading contenders for code-based KEM in Round 3 of the NIST standardization competition, announced in July 2020, including Classic McEliece, BIKE, and HQC, as well as second round contender LEDAcrypt.For each of these proposals, we describe in detail the problem on which the security is based, the system parameters, key generations, encapsulation, decapsulation, attacks on the system, and a security analysis.Chapter 4 concludes with a summary of the findings and includes some potential areas for future research.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.261
Teacher spread0.246 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations1
Published2020
Admission routes1
Has abstractyes

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