Efficient and Secure Delegation of Linear Algebra.
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
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
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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