Achieving Data Privacy through Secrecy Views and Null-Based Virtual Updates
Bibliographic record
Abstract
There may be sensitive information in a relational database, and we might want to keep it hidden from a user or group thereof. In this work, sensitive data is characterized as the contents of a set of secrecy views. For a user without permission to access that sensitive data, the database instance he queries is updated to make the contents of the views empty or contain only tuples with null values. In particular, if this user poses a query about any of these views, no meaningful information is returned. Since the database is not expected to be physically changed to produce this result, the updates are only virtual. And also minimal in a precise way. These minimal updates are reflected in the secrecy view contents, and also in the fact that query answers, while being privacy preserving, are also maximally informative. Virtual updates are based on the use of null values as used in the SQL standard. We provide the semantics of secrecy views and the virtual updates. The different ways in which the underlying database is virtually updated are specified as the models of a logic program with stable model semantics. The program becomes the basis for the computation of the "secret answers" to queries, i.e. those that do not reveal the sensitive information.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".