MétaCan
Menu
Back to cohort
Record W2904756193 · doi:10.1145/3229061

Type-Driven Gradual Security with References

2018· article· en· W2904756193 on OpenAlexaff
Matías Toro, Ronald Garcia, Éric Tanter

Bibliographic record

VenueACM Transactions on Programming Languages and Systems · 2018
Typearticle
Languageen
FieldComputer Science
TopicSecurity and Verification in Computing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceProgramming languageStatic analysisCode refactoringSimple (philosophy)Type (biology)Type safetyType inferenceCode (set theory)Theoretical computer scienceArtificial intelligenceSoftware

Abstract

fetched live from OpenAlex

In security-typed programming languages, types statically enforce noninterference between potentially conspiring values, such as the arguments and results of functions. But to adopt static security types, like other advanced type disciplines, programmers face a steep wholesale transition, often forcing them to refactor working code just to satisfy their type checker. To provide a gentler path to security typing that supports safe and stylish but hard-to-verify programming idioms, researchers have designed languages that blend static and dynamic checking of security types. Unfortunately, most of the resulting languages only support static, type-based reasoning about noninterference if a program is entirely statically secured. This limitation substantially weakens the benefits that dynamic enforcement brings to static security typing. Additionally, current proposals are focused on languages with explicit casts and therefore do not fulfill the vision of gradual typing, according to which the boundaries between static and dynamic checking only arise from the (im)precision of type annotations and are transparently mediated by implicit checks. In this article, we present GSL Ref , a gradual security-typed higher-order language with references. As a gradual language, GSL Ref supports the range of static-to-dynamic security checking exclusively driven by type annotations, without resorting to explicit casts. Additionally, GSL Ref lets programmers use types to reason statically about termination-insensitive noninterference in all programs, even those that enforce security dynamically. We prove that GSL Ref satisfies all but one of Siek et al.’s criteria for gradually-typed languages, which ensure that programs can seamlessly transition between simple typing and security typing. A notable exception regards the dynamic gradual guarantee, which some specific programs must violate if they are to satisfy noninterference; it remains an open question whether such a language could fully satisfy the dynamic gradual guarantee. To realize this design, we were led to draw a sharp distinction between syntactic type safety and semantic type soundness , each of which constrains the design of the gradual language.

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)0.008
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0020.009
Scholarly communication0.0050.013
Open science0.0040.011
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0040.002

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 head0.033
GPT teacher head0.301
Teacher spread0.268 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations45
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueACM Transactions on Programming Languages and SystemsSame topicSecurity and Verification in ComputingFrench-language works237,207