Efficient Fully Homomorphic Encryption with Large Plaintext Space
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
The security of multimedia content and personal privacy for big data has triggered widespread concern in the society. Fully homomorphic encryption (FHE), which can homomorphically compute arbitrary functions on the encrypted data without knowing the secret key, is valuable in protecting user's data security. However, most of the FHE schemes only take single-bit of ciphertext as the input, which makes the evaluation process complicated. In EUROCRYPT'2015, Ducas and Micciancio proposed an FHE scheme FHEW with the plaintext space Z2, and gave an assumption of extending the plaintext space to Zt. In this paper, we optimize the decryption algorithm of FHEW in bootstrapping, and propose an FHE scheme with large plaintext space Zt. Firstly, we optimize the rounding function of the decryption algorithm in FHEW to the msdExtract algorithm, which can homomorphically extract the most significant digit of the plaintext. Secondly, we design the msdExtract algorithm by employing the homomorphic accumulator, and present the process of general bootstrapping. Finally, based on the msdExtract algorithm, we extend the plaintext space of our scheme to Zt, comparing to Z2 in FHEW. The security of our scheme is based on the basic LWE scheme and FHEW. What's more, our scheme can perform the evaluation more conveniently with large plaintext space, and can be applied to more scenarios.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it