Least-restrictive enforcement of the Chinese wall security policy
Bibliographic record
Abstract
The Chinese Wall security policy states that information from objects that are to be confidential from one another should not flow to a subject. It addresses conflict of interest, and was first articulated in the well-cited work of Brewer and Nash, which proposes also an enforcement mechanism for the policy. Work subsequent to theirs has observed that their enforcement mechanism is overly restrictive -- authorization states in which the policy is not violated may be rendered unreachable. We present two sets of novel results in this context. In one, we present an enforcement mechanism for the policy that is simple and efficient, and least-restrictive -- an authorization state is reachable if and only if it does not violate the policy. In our enforcement mechanism, the actions of a subject can constrain the prospective actions of another, a trade-off that we show every enforcement mechanism that is least-restrictive must incur. Our other set of results is that the enforcement mechanism of Brewer-Nash is even more restrictive than previous work establishes. Specifically, we show: (1) what is called the *-rule is overspecified in that one of its sub-rules implies the other, and, (2) if a subject is authorized to write to an object that contains confidential information, then all objects that contain confidential information must belong to the same conflict of interest class. Our work sheds new light on what is generally considered to be important work in information security.
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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".