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Record W2374313563

Privacy Protecting Algorithm and Simulation Based on Chaotic Sequence

2006· article· en· W2374313563 on OpenAlex
Shiyong Zhu

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.

venuePublished in a venue whose home country is Canada.
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

VenueMicrocomputer applications · 2006
Typearticle
Languageen
FieldComputer Science
TopicChaos-based Image/Signal Encryption
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceCHAOS (operating system)ChaoticKey (lock)Sequence (biology)AlgorithmPixelImage (mathematics)Computer securityTheoretical computer scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

In order to protect privacy from taking illegal pictures and investigate the peacebreaker who exposes the privacy, the distribution hidden algorithm of secret key based on chaos cryptogram is introduced in the paper. The information of mobile telephone is hidden in the illegal pictures while being taken through cryptic algorithm. The chaos sequence is produced through chaos algorithm, and is distributed to every row' according to distributing secret key. The initial lines are gotten by converting chaos sequence into natural number. The lower bits of image pixels determined by the rows and lines are replaced by hidden data bits. Then the information of mobile telephone hidden in open private pictures is identifiedthrough detective algorithm. The simulation results indicate there is no difference between the original pictures and the information-hidden pictures.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.708
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.260
Teacher spread0.244 · 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