OUF: Oblivious Universal Function with domain specific optimizations
Bibliographic record
Abstract
The growing need for secure computation has spurred interest in cryptographic techniques that operate on encrypted data without revealing its content. Fully Homomorphic Encryption (FHE), especially LWE-based schemes, enables such processing while preserving confidentiality. Decentralized computing offers scalable resources without requiring in-house servers, but it relies heavily on the confidentiality guarantees of underlying schemes. While many existing protocols successfully protect input privacy, function confidentiality remains a largely overlooked but crucial aspect of secure delegated computation.In this work, we present a novel Oblivious Universal Function (OUF) scheme that enables a client to outsource computation of an arbitrary function to an untrusted server while hiding both the input data and the function being applied. Our construction leverages LWE-based FHE and a virtual-tape evaluation model to support composable, non-interactive, and reusable function execution. Crucially, the server remains oblivious not only to the encrypted inputs but also to the structure, type, and identity of the function it is executing. OUF thus bridges the gap between theoretical privacy guarantees and practical secure computation in decentralized environments.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".