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Record W2900722080 · doi:10.1080/17445760.2018.1550771

How to encrypt a graph

2018· article· en· W2900722080 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

VenueInternational Journal of Parallel Emergent and Distributed Systems · 2018
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsQueen's University
Fundersnot available
KeywordsPlaintextEncryptionCiphertextComputer scienceCommunication sourceTheoretical computer scienceGraphAlgorithmComputer network

Abstract

fetched live from OpenAlex

An algorithm is described for encrypting a graph to be transmitted securely from a sender to a receiver. In communications terminology, “the graph is the message”: its vertices, its edges, and its edge weights are the information to be concealed. The encryption algorithm is based on an unconventional mapping, conjectured to be a trapdoor one-way function, designed for graphs. This function requires the sender and the receiver to use a secret one-time encryption/decryption key. It is claimed that a malicious eavesdropper with no knowledge of the key will be faced with a computational task requiring exponential time in the size of the input graph in order to extract the original plaintext from the ciphertext carried by the encrypted graph. A number of variants to the main algorithm are also proposed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.447

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 science0.0010.000
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 head0.018
GPT teacher head0.269
Teacher spread0.250 · 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