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Record W2096230411 · doi:10.1109/prdc.2006.58

Storing RSA Private Keys In Your Head

2006· article· en· W2096230411 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicChaos-based Image/Signal Encryption
Canadian institutionsAcadia University
Fundersnot available
KeywordsPublic-key cryptographyKey (lock)CryptosystemComputer scienceComputer securityKey generationThreshold cryptosystemProcess (computing)CryptographyEncryptionPublic key cryptosystemOperating system

Abstract

fetched live from OpenAlex

An issue when using the RSA public-key system is that for reasonable levels of security, both the public key and the private key must be quite large. Since few people are capable of memorizing a 1024-bit private key, most people must store this number on a computer hard drive or other digital storage device. There are (at least) three problems with this; the first is that if your computer is even temporarily compromised, your private key could be stolen. The second is that if you are away from your computer (and don't have your key on a portable storage device) but need your private key to access some resource, you are unable to do so. The third is that your key could be irrevocably lost because of hardware problems. This paper describes a way of generating a public/private RSA key pair from a passphrase to overcome these problems. Although the paper's focus is on the generation of RSA keys, the process can be applied to any cryptosystem (symmetric or asymmetric) which relies on random data for generating keys

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.642
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.259
Teacher spread0.238 · 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

Quick stats

Citations1
Published2006
Admission routes1
Has abstractyes

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