Secure Normal Form: Mediation Among Cross Cryptographic Leakages in Encrypted Databases
Bibliographic record
Abstract
Existing secure data outsourcing systems offer users ways to select from different cryptographic primitives supported by the system to encrypt their data to strike a balance between data confidentiality and query performance. Though prior work have identified the danger of mixing cryptographic primitives, they fall short of providing a systematic approach to guide users to prevent such cross-cryptographic leakages. Inspired by the database design theory, we envision Secure Normal Form, a new approach to normalize encrypted databases such that the leakages of the partitioned databases are limited to the users' specifications. In this work, we propose a new architecture to support secure normal form. This system includes several new components for secure data outsourcing: (i) an inference mechanism that reasons about additional leakages from weaker encryption techniques, based on semantic data properties (e.g., dependence between attribute values); (ii) a normalization mechanism that converts relational data into secure normal forms, so that the information leaked by the representation is limited to that specified by the user; and (iii) a secure query execution approach over encrypted data in secure normal forms. Our initial experimental results validate the performance improvement over naïve baseline and show that a careful data representation can be allowed without compromising security. We believe that our paper opens a new direction in secure data management.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.024 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".