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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.001 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".