Towards Cloud Data Warehouses of Multivalued Encrypted Values
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The motivation behind generating multivalued encrypted values by multivalued encrypted techniques (METs) is the minimization of encrypted data redundancy. In the literature, there are METs that enable the processing of sum aggregations, selection constraints and sorting operations over multivalued encrypted values. However, we found a lack of METs for processing data groupings over multivalued encrypted values. In this article, we propose two novel METs that allow the execution of data groupings over multivalued encrypted values and we specify two novel data schemas for encrypted DWs (MV-HOM and MVSE-HOM) that allow analytical queries with data groupings be performed over multivalued encrypted values. Also, we conducted a performance evaluation of encrypted DWs stored in a cloud and results showed that MV-HOM's overhead was decreased up to 10.41% (with 16 nodes) when compared to a non-encrypted dimensional data schema, while the overhead imposed by MVSE-HOM was high (89.91% with 16 nodes).
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.
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 | 0.002 | 0.000 |
| 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 | 0.008 |
| Open science | 0.003 | 0.003 |
| 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