Efficient and Privacy-Preserving Speaker Recognition for Cybertwin-Driven 6G
Bibliographic record
Abstract
With the introduction of cybertwin, a new approach to represent human or things in the cyberspace, it is foreseeable that vehicles will be able to offer more and more services in the future. Naturally, considering the safety of drivers, speaker recognition will be widely used in vehicle scenarios. Speaker recognition technologies are experiencing increasing popularity due to the unique and indissoluble link between individuals and their voices. However, the coming cybertwin-driven 6G brings speaker recognition technologies unprecedented challenges, especially in preventing the disclosure of voiceprint. To address these challenges, an efficient and privacy-preserving speaker recognition scheme for cybertwin-driven 6G, referred to as NEATEN, is proposed in this article. With NEATEN, the speaker identity can be recognized at multiple security levels without leaking the voiceprint data. More concretely, based on the random projection data perturbation, voiceprint perturbation algorithms in two phases and the corresponding ciphertext-based similarity computation algorithm are proposed. By using these algorithms, our efficient and accurate speaker recognition scheme can be achieved. Orthogonal to the previous works of biometric identification based on the Euclidean distance, NEATEN makes progress on the non-Euclidean distance, such as cosine distance and complicated distance. Detailed analysis shows that NEATEN can resist various known security threats. Experiments conducted on TIMIT and Voxceleb data sets have demonstrated that NEATEN is highly accurate and efficient, and can be flexibly deployed in a real cybertwin-driven 6G vehicle environment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".