vFHE: Verifiable Fully Homomorphic Encryption
Bibliographic record
Abstract
Fully Homomorphic Encryption (FHE) is a powerful building block for secure and private applications. However, state-of-the-art FHE schemes do not offer any integrity guarantees, which can lead to devastating correctness and security issues when FHE is deployed in non-trivial settings. In this paper, we take a critical look at existing integrity solutions for FHE, and analyze their (often implicit) threat models, efficiency, and adequacy with real-world FHE deployments. We explore challenges of what we believe is the most flexible and promising integrity solution for FHE: namely, zero-knowledge Succinct Non-interactive ARguments of Knowledge (zkSNARKs); we showcase optimizations for both general-purpose zkSNARKs and zkSNARKs designed for FHE. We then present two software frameworks, circomlib-FHE and zkOpenFHE, which allow practitioners to automatically augment existing FHE pipelines with integrity guarantees. Finally, we leverage our tools to evaluate and compare different approaches to FHE integrity, and discuss open problems that stand in the way of a widespread deployment of FHE in real-world applications.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".