Bibliographic record
Abstract
Flow-sensitive dynamic enforcement mechanisms for information flow labels offer increased permissiveness. However, these mechanisms may leak sensitive information when deciding to block insecure executions. When enforcing two labels (e.g., secret and public), sensitive information is leaked from the context in which this decision is taken. When enforcing arbitrary labels, additional sensitive information is leaked from the labels involved in the decision to block an execution. We give examples where, contrary to a common belief, a mechanism designed to enforce two labels may not be able to enforce arbitrary labels, due to this additional leakage. In fact, it is not trivial to design a dynamic enforcement that offers increased permissiveness, handles multiple labels, and does not introduce information leakage due to blocking insecure executions. In this paper, we present a dynamic enforcement mechanism of information flow labels that has all these three attributes. Our mechanism is not purely dynamic, since it uses a light-weight, on-the-fly, static analysis of untaken branches. We prove that the set of all normally terminated and blocked traces of a program, which is executed under our mechanism, satisfies noninterference, against principals that make observations throughout execution.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".