The Secrecy Resilience of Access Control Policies and Its Application to Role Mining
Bibliographic record
Abstract
We propose a notion that we call the secrecy resilience of an access control policy that, to our knowledge, has not been explored in prior work. We seek to capture with this notion the property inherent to an access control policy that measures its resistance to disclosure. We motivate and then propose a definition for secrecy resilience that is based on the notion of entropy from information theory. We focus on policies expressed in Role-Based Access Control (RBAC), and contrast RBAC from the access matrix from the standpoint of secrecy resilience. We observe that similar to other objectives such as the minimization of the number of roles, an RBAC policy with the best secrecy resilience can be a desirable objective of bottom-up role-mining, with which we seek to compute an RBAC policy given as input an access matrix. We have carried out an empirical assessment of several role-mining algorithms from the standpoint of secrecy resilience for two underlying distribution-events pairs each of which captures a kind of best-case from the standpoint of a defender. Towards carrying out the empirical assessment, we make an additional contribution to role-mining: we propose new reductions for the two problems of minimizing the number of roles and the number of edges, and discuss the manner in which our reductions are superior to reductions in existing work.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".