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Record W2030094967 · doi:10.1109/hpcc.2014.140

Proof of Retrieval and Ownership Protocols for Images through SPIHT Compression

2014· article· en· W2030094967 on OpenAlexaff
Fatema Rashid, Ali Miri, Isaac Woungang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCloud Data Security Solutions
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceEncoding (memory)Protocol (science)Data compressionCloud computingSet partitioning in hierarchical treesImage compressionCompression (physics)Scheme (mathematics)EncryptionTheoretical computer scienceInformation retrievalData miningImage (mathematics)Computer visionComputer securityArtificial intelligenceOperating systemImage processingMathematics

Abstract

fetched live from OpenAlex

In this paper, novel proof of retrieval (POR) and proof of ownership (POW) protocols for images based on the use of the SPIHT compression algorithm are proposed. The POR protocol can be run by the users to ensure that their images are stored securely in the cloud, and the POW protocol can be run by the cloud storage provider (CSP) to authenticate the true owner of the images. The efficiency of our scheme relies on the fact that only a fraction of the compressed data is used for encoding the images. Experimental results are provided to validate the stated goals.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.011
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.051
GPT teacher head0.329
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2014
Admission routes1
Has abstractyes

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