Federated Speaker Verification via Differential Privacy
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Solution Overview
Problem
Existing speaker verification systems face challenges in protecting user privacy due to centralized data storage, leading to potential data leakage and compliance issues with privacy regulations, while current privacy protection methods are either computationally complex, reduce recognition accuracy, or require high network stability.
Innovation Solution
A federated speaker verification method based on differential privacy, where a server pre-trains a universal background model (UBM) and sends it to clients for local learning, with differential privacy protection added to statistics before upload, allowing for aggregation and updating without exposing private data, enabling secure model training and verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user speech data is uploaded to servers for centralized training, then model training effectiveness is improved, but user privacy security deteriorates
Solution Approach 1:
The centralized training process is segmented into distributed federated learning across multiple clients. Each client trains locally on their own speech data without uploading raw data to the server. Only model parameters (gradients) are transmitted and aggregated by the server, thereby maintaining model training effectiveness while eliminating the privacy leakage risk associated with centralized data storage.
Solution Approach 2:
Differential privacy is introduced as an intermediary mechanism between the client's local model updates and the server's aggregation process. Noise is added to the gradients before transmission, acting as a mediator that prevents the server from reconstructing user speech data while still allowing effective model learning to occur.
2Object-affected harmful factors
If homomorphic encryption is used to protect user private information, then privacy security is improved, but computational complexity increases
Solution Approach 1:
Instead of using computationally expensive homomorphic encryption throughout the entire federated learning process, differential privacy with Laplace noise is employed as a lighter, more efficient alternative. This approach provides adequate privacy protection for the gradient transmission stage without the heavy computational overhead of homomorphic encryption, making the system more practical for widespread deployment.
3Object-affected harmful factors
If differential privacy noise is added to statistics, then privacy protection is improved, but model training accuracy may deteriorate
Solution Approach 1:
The differential privacy mechanism applies noise at the gradient level rather than at the final model output level. This partial application of privacy protection allows the model to learn from the noisy gradients while still achieving good accuracy. The noise level is carefully calibrated to provide sufficient privacy protection without excessively degrading model performance.
Solution Approach 2:
The federated learning process incorporates multiple rounds of interaction between clients and server, where the server aggregates noisy gradients from multiple clients and sends updated model parameters back to clients. This feedback loop allows the model to gradually converge to accurate solutions despite the noise added for privacy protection in each individual round.
Data Source
AI summary
The present disclosure proposes a federated speaker verification method based on differential privacy, including: 1. performing, by a server, UBM pre-training to obtain an initial UBM; 2. receiving, by the client, the pre-trained initial UBM, and performing initial UBM learning based on local private speech data; 3. performing, by the client, differential privacy protection based on learned statistics; 4. aggregating, by the server, statistics uploaded by multiple clients, and updating the initial UBM; and 5. receiving, by the client, the updated UBM, performing adjustment based on the local private speech data to obtain a GMM for a user of the client, and determining, based on the updated UBM and the GMM, whether a to-be-verified speech is generated by the user of the client.