PSALM: Cardinality Estimation in the Presence of Fine-Grained Access Controls
Why this work is in the frame
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Bibliographic record
Abstract
In database systems that support fine-grained access controls, each user has access rights that determine which tuples are accessible and which are inaccessible. Queries are answered as if the inaccessible tuples are not present in the database. Thus, users with different access rights may get different answers to a given query. To process queries efficiently in the presence of fine-grained access controls, the database system needs accurate estimates of the number of tuples that are both accessible according to the access rights of the submitting user and relevant according to the selection predicates in the query. In this paper, we present PSALM, a sampling-based cardinality estimation technique for use in the presence of fine-grained access controls. Our technique exploits the fact that access rights are relatively static and are common to all queries that are evaluated on behalf of a particular user. We show that PSALM provides more accurate estimates than techniques that do not exploit knowledge of access rights.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it