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Record W3110590623

Removable Weak Keys for Discrete Logarithm Based Cryptography.

2020· preprint· en· W3110590623 on OpenAlexaff
Michael John Jacobson, Prabhat Kushwaha

Bibliographic record

VenueIACR Cryptology ePrint Archive · 2020
Typepreprint
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDiscrete logarithmCryptosystemPublic-key cryptographyCryptographyLogarithmMathematicsTheoretical computer scienceDiscrete mathematicsComputer scienceKey (lock)Security parameterEncryptionComputer security
DOInot available

Abstract

fetched live from OpenAlex

We describe a novel type of weak cryptographic private key that can exist in any discrete logarithm-based public-key cryptosystem set in a group of prime order p where $$p-1$$ has small divisors. Unlike the weak private keys based on numerical size (such as smaller private keys, or private keys lying in an interval) that will always exist in any DLP cryptosystems, our type of weak private keys occurs purely due to parameter choice of p, and hence, can be removed with appropriate value of p. Using the theory of implicit group representations, we present a method to determine whether a public key comes from a weak private key subject to a given computational bound, and if so, recover the private key from the corresponding public key. We analyze several elliptic curves proposed in the literature and in various standards, giving counts of the number of keys that can be broken with relatively small amounts of computation.. Our results show that many of these curves, including some from standards, have a considerable number of such weak private keys. We also use our methods to show that none of the 14 outstanding Certicom Challenge problem instances are weak in our sense, up to a certain weakness bound.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.090
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0050.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.279
Teacher spread0.251 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2020
Admission routes1
Has abstractyes

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