Bibliographic record
Abstract
We consider secure delegation of linear algebra computation, wherein a client, privately and verifiably, outsources tasks such as matrix multiplication, matrix inversion, computing the rank and determinant, and solving a linear system to a remote worker. When operating on n×n matrices, we design non-interactive, and secure protocols for delegating matrix multiplication, based on a number of encryption schemes with limited homomorphic properties where the client only needs to perform O(n 2) work. The main component of these delegation protocols is a mechanism for efficiently verifying the homomorphic matrix multiplication performed by the worker. We introduce a general method for performing this verification, for any homomorphic encryption scheme that satisfies two special properties. We then show that most existing homomorphic encryption schemes satisfy these properties and hence can utilize our general verification method. In case of the BGN-style encryption of [Gentry et al., EUROCRYPT 2010], we also show a simpler and more efficient verification method that does not follow our general approach. Finally, we show constant round and efficient constructions for secure delegation of other linear algebra tasks based on our delegation protocol for matrix multiplication. In all of these constructions, the client’s
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".