PEKSrand: Providing Predicate Privacy in Public-key Encryption with Keyword Search.
Bibliographic record
Abstract
notion of predicate privacy, i.e., the property that t(x) reveals no information about the encoded predicate p, and proposed a scheme that achieves predicate privacy in the symmetric-key settings. In this paper, we propose two schemes. In the first scheme, we extend PEKS to support predicate privacy based on the idea of randomization. To the best of our knowledge, this is the first work that ensures predicate privacy in the publickey settings without requiring interactions between the receiver and potential senders, the size of which may be very large. Moreover, we identify a new type of attacks against PEKS, i.e., statistical guessing attacks. Accordingly, we introduce a new notion called statistics privacy, i.e., the property that predicate privacy is preserved even when the statistical distribution of keywords is known, and propose a scheme that makes a tradeoff between statistics privacy and storage efficiency (of the delegate). According to our analysis and experimental results, compared to PEKS, both of our schemes introduce reasonable additional communication and computation overheads and can be smoothly deployed in existing systems. I.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".