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Record W4288057739 · doi:10.1109/sp46214.2022.9833601

Sabre: Sender-Anonymous Messaging with Fast Audits

2022· article· en· W4288057739 on OpenAlexaff
Adithya Vadapalli, Kyle Storrier, Ryan Henry

Bibliographic record

Venue2022 IEEE Symposium on Security and Privacy (SP) · 2022
Typearticle
Languageen
FieldComputer Science
TopicInternet Traffic Analysis and Secure E-voting
Canadian institutionsUniversity of CalgaryUniversity of Waterloo
Fundersnot available
KeywordsCommunication sourceAuditComputer scienceComputer securityInternet privacyComputer networkBusinessAccounting

Abstract

fetched live from OpenAlex

We present Sabre, a family of sender-anonymous messaging protocols with instances supporting both Twitter-like anonymous bulletin boards and Secure Drop-like mailboxes. Both variants provide strong anonymity while potentially scaling to anonymity sets in the tens of millions and beyond. Sabre protocols inherit their basic structure from Riposte and Express while replacing those schemes’ linear-cost audit protocols with new logarithmic-cost ones. Sabre also incorporates a host of innovations that improve concrete performance by an order of magnitude or more under “ideal” circumstances, while providing asymptotic speedups in the face of resource exhaustion-style DoS attacks.

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)0.005
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.006
Open science0.0030.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.003

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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations17
Published2022
Admission routes1
Has abstractyes

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