Bibliographic record
Abstract
The federated learning paradigm protects private data from explicit leakage, yet exposing the model weights still raises serious privacy concerns with well-known attacks, such as membership inference attacks. It has been acknowledged that mechanisms such as homomorphic encryption and differential privacy can be adopted to provide a higher level of protection. However, these mechanisms may incur a formidable amount of overhead and reductions in training performance, which make them unlikely to be employed in real-world applications. In this paper, we proposeMaskCrypt, a new mechanism designed to balance the trade-off between security and practicality when homomorphic encryption is used. Rather than encrypting model updates in their entirety,MaskCryptapplies an encryption mask to sift out a small portion of the updates for encryption. Specifically, eachMaskCryptclient adopts a gradient-guided mechanism to select the encryption mask, which aims to obfuscate the training trace by maximizing the local loss value of exposed model weights, and then sending the individual mask to a specialMask Consensusmechanism to obtain a final mask for all clients. Our experimental results have shown convincing evidence that with a small encrypt ratio,MaskCryptreduced the communication overhead by up to 4.15× compared with encrypting entire model updates, yet still effectively protected the client's private data against inversion attacks, and reduced the accuracy of membership inference attacks to 49.2%.w
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".