Efficient secure query evaluation over encrypted XML databases
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
Motivated by the database-as-service paradigm wherein data owned by a client is hosted on a third-party server, there is significant interest in secure query evaluation over encrypted databases. We consider this problem for XML databases. We consider an attack model where the attacker may possess exact knowledge about the domain values and their occurrence frequencies, and we wish to protect sensitive structural information as well as value associations. We capture such security requirements using a novel notion of security constraints. For security reasons, sensitive parts of the hosted database are encrypted. There is a tension between data security and efficiency of query evaluation for different granularities of encryption. We show that finding an optimal, secure encryption scheme is NP-hard. For speeding up query processing, we propose to keep metadata, consisting of structure and value indices, on the server. We want to prevent the server, or an attacker who gains access to the server, from learning sensitive information in the database. We propose security properties for such a hosted XML database system to satisfy and prove that our proposal satisfies these properties. Intuitively, this means the attacker cannot improve his prior belief probability distribution about which candidate database led to the given encrypted database, by looking at the encrypted database as well as the metadata. We also prove that by observing a series of queries and their answers, the attacker cannot improve his prior belief probability distribution over which sensitive queries (structural or value associations) hold in the hosted database. Finally, we demonstrate with a detailed set of experiments that our techniques enable efficient query processing while satisfying the security properties defined in the paper.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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