Large-Scale Recurrent Neural Networks with Fully Homomorphic Encryption for Privacy-Enhanced Speaker Identification
Bibliographic record
Abstract
Temporal classification tasks such as speaker identification are often performed by recurrent neural networks (RNNs) that observe potentially private (sensitive) data in order to provide service. Although encrypting this data safeguards it during storage and transit, decryption for computation introduces a potential vulnerability. Fully homomorphic encryption (FHE) is a privacy-enhancing technology that supports computation over encrypted data. A neural network with multiple RNN layers and attention over encrypted data for this task is presented. Using GPU acceleration and novel contributions: (1) a RNN quantization procedure with ternarized parameters and binarized activations, and (2) a ciphertext-ciphertext multiplication method for attention that reduces required computation by 50%, yields the first published multi-layer RNN with attention over encrypted data. This marks a significant step toward practical privacy-enhanced temporal classification.
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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 | 0.000 |
| 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".