MétaCan
Menu
Back to cohort
Record W7110032274 · doi:10.4230/lipics.itc.2025.2

Powerful Primitives in the Bounded Quantum Storage Model

2025· article· en· W7110032274 on OpenAlexaff

Bibliographic record

VenueDROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2025
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLearning with errorsEncryptionConstruct (python library)Bounded functionQuantumKey (lock)Transmission (telecommunications)Quantum computerQuantum cryptography

Abstract

fetched live from OpenAlex

The bounded quantum storage model aims to achieve security against computationally unbounded adversaries that are restricted only with respect to their quantum memories. In this work, we provide the following contributions in this model: 1) We build one-time programs and utilize them to construct CCA1-secure symmetric key encryption and message authentication codes. These schemes require no quantum memory from honest users, yet they provide information-theoretic security against adversaries with arbitrarily large quantum memories, as long as the transmission length is suitably large. 2) We introduce the notion of k-time program broadcast which is a form of program encryption that allows multiple users to each learn a single evaluation of the encrypted program, while preventing any one user from learning more than k evaluations of the program. We build this primitive unconditionally and employ it to construct CCA1-secure asymmetric key encryption, encryption tokens, signatures, and signature tokens. All these schemes are information-theoretically secure against adversaries with roughly e^√m quantum memory where m is the quantum memory required for the honest user. All of the constructions additionally satisfy disappearing security, essentially preventing an adversary from storing and using a transmission later on.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0000.001
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 head0.013
GPT teacher head0.271
Teacher spread0.258 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueDROPS (Schloss Dagstuhl – Leibniz Center for Informatics)Same topicCryptography and Data SecurityFrench-language works237,207