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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 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
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.

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: Methods · Consensus signal: none
Teacher disagreement score0.775
Threshold uncertainty score0.252

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 communication0.0000.000
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 head0.051
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

Quick stats

Citations9
Published2014
Admission routes1
Has abstractyes

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