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Record W7133015008

Constructing pseudorandom function generators

2008· dissertation· W7133015008 on OpenAlex
J. J. Bronson

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTSpace · 2008
Typedissertation
Language
FieldComputer Science
TopicChaos-based Image/Signal Encryption
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsPseudorandom generator theoremPseudorandom number generatorPseudorandom generatorPseudorandom function familyPseudorandomnessRandom seedFunction (biology)Pseudorandom permutation
DOInot available

Abstract

fetched live from OpenAlex

Pseudorandom number generators are often composed to yield pseudorandom function generators. We will introduce a model in which it should be possible to demonstrate a lower bound on the number of times a pseudorandom function generator must query an oracle for a pseudorandom number generator. Weakly pseudorandom function generators are often composed in rounds to produce adaptively pseudorandom function generators. Non-adaptively pseudorandom function generators were introduced to model the weakly secure function generators. In [18], using an assumption related to the non-uniform DDH assumption, it is shown that the composition of non-adaptively pseudorandom function generators is not adaptively pseudorandom in general. We will show that the assumption given in [18] can be weakened to the more standard non-uniform DDH assumption, yielding a stronger result.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.658
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.022
GPT teacher head0.295
Teacher spread0.273 · 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