Communication-efficient public key encryption with (fine-grained delegated) equality test
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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