Secure Publishing using Schema-level Role-based Access Control Policies for Fragments of XML Documents
Bibliographic record
Abstract
Popularity of social networks is growing rapidly and secure publishing is an important implementation tool for these networks. At the same time, recent implementations of access control policies (ACPs) for sharing fragments of XML documents have moved from distributing to users numerous sanitized sub-documents to disseminating a single document multi-encrypted with multiple cryptographic keys, in such a way that the stated ACPs are enforced. Any application that uses this implementation of ACPs will incur a high cost of generating keys separately for each document. However, most such applications, such as secure publishing, use similar documents, i.e. documents based on a selected schema. This paper describes RBAC defined at the schema level, (SRBAC), and generation of the minimum number of keys at the schema level. The main advantage of our approach is that for any application that uses a fixed number of schemas, keys can be generated (or even pre-generated) only once, and then reused in all documents valid for the given schema. While in general, key generation at the schema level has to be pessimistic, our approach tries to minimize the number of generated keys. Incoming XML documents are efficiently encrypted using single-pass SAX parsing in such a way that the original structure of these documents is completely hidden. We also describe distributing to each user only keys needed for decrypting accessible nodes, and for applying the minimal number of encryption operations to an XML document required to satisfy the protection requirements of the policy.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".