Communication-efficient public key encryption with (fine-grained delegated) equality test
Bibliographic record
Abstract
Abstract With the rise of cloud storage and the looming threat of quantum computing, traditional encryption methods are encountering significant challenges that hinder data manipulation without decryption. To counter quantum attacks while maintaining data manipulation capabilities, new architectures such as quantum-resistant public key encryption with equality test (PKEET) must be developed. Our study presents the initial PKEET that leverages the Learning with Rounding (LWR) problem, which provides security within standard model. We also introduce its variants, public key encryption with delegated equality test (PKE-DET) and PKEET supporting flexible authorization (PKEET-FA). Our proposals could achieve fine-grained delegation at the ciphertext-specified level compared to previous PKE-DET schemes. For example, our PKE-DET supports a delegated tester function while ensuring security against quantum computing threats. Our PKEET-FA could accord users even more controls over what ciphertexts they want to compare. Our schemes’ security is founded on the LWR problem which avoids the need for discrete Gaussian sampling, unlike the Learning with Errors (LWE) problem. This distinction renders our methods both simpler and more efficient compared to those based on LWE. Moreover, our schemes enjoy smaller-sized ciphertexts.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".