User-Level Differential Privacy against Attribute Inference Attack of Speech Emotion Recognition in Federated Learning

Understand

Many existing privacy-enhanced speech emotion recognition (SER) frameworks focus on perturbing the original speech data through adversarial training within a centralized machine learning setup.

• However, this privacy protection scheme can fail since the adversary can still access the perturbed data.
• In recent years, distributed learning algorithms, especially federated learning (FL), have gained popularity to protect privacy in machine learning applications.
• While FL provides good intuition to safeguard privacy by keeping the data on local devices, prior work has shown that privacy attacks, such as attribute inference attacks, are achievable for SER systems trained using FL.