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Record W3100371214 · doi:10.1016/j.procs.2020.10.070

An Effective and Efficient Technique for Supporting Privacy-Preserving Keyword-Based Search over Encrypted Data in Clouds

2020· article· en· W3100371214 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProcedia Computer Science · 2020
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la RechercheUniversity of Manitoba
KeywordsComputer scienceEncryptionPlaintextCloud computingKeyword searchInformation retrievalMatching (statistics)DatabaseComputer security

Abstract

fetched live from OpenAlex

Nowadays, cloud providers offer to their clients the possibility of storage of emails and files on the cloud server. To avoid privacy concerns, encryption should be applied to data. Unlike searching plaintext documents by keywords, encrypted documents cannot be retrieved in the same manner. As keyword searches on encrypted data are in demand, this paper describes an effective and efficient technique to support privacy-preserving keyword-based search over encrypted outsourced data. With this technique, encrypted data are first searched with the keyword, support for dynamic operations is then checked, and all relevant data documents are finally sorted based on the number of keywords matching the user query. To evaluate the technique, precision and recall are measured. The results reveal the effectiveness and efficiency of the technique in supporting privacy-preserving keyword-based search over encrypted outsourced data.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0060.004
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 head0.030
GPT teacher head0.320
Teacher spread0.290 · 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