MaskCrypt: Federated Learning With Selective Homomorphic Encryption
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
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 propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MaskCrypt</small> , 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, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MaskCrypt</small> applies an encryption mask to sift out a small portion of the updates for encryption. Specifically, each <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MaskCrypt</small> client 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 special <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mask Consensus</i> mechanism to obtain a final mask for all clients. Our experimental results have shown convincing evidence that with a small encrypt ratio, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MaskCrypt</small> reduced 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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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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