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Record W2054801486 · doi:10.5555/1251028.1251041

A security model for full-text file system search in multi-user environments

2005· article· en· W2054801486 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
TopicAdvanced Data Storage Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceSearch engine indexingImplementationPermissionFile systemUnix file typesComputer fileIndex (typography)Operating systemDatabaseStub fileInformation retrievalWorld Wide WebProgramming language

Abstract

fetched live from OpenAlex

Most desktop search systems maintain per-user indices to keep track of file contents. In a multi-user environment, this is not a viable solution, because the same file has to be indexed many times, once for every user that may access the file, causing both space and performance problems. Having a single system-wide index for all users, on the other hand, allows for efficient indexing but requires special security mechanisms to guarantee that the search results do not violate any file permissions. We present a security model for full-text file system search, based on the UNIX security model, and discuss two possible implementations of the model. We show that the first implementation, based on a postprocessing approach, allows an arbitrary user to obtain information about the content of files for which he does not have read permission. The second implementation does not share this problem. We give an experimental performance evaluation for both implementations and point out query optimization opportunities for the second one. 1

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.528
Threshold uncertainty score0.479

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 communication0.0000.001
Open science0.0010.001
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 head0.036
GPT teacher head0.280
Teacher spread0.245 · 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

Citations24
Published2005
Admission routes1
Has abstractyes

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