TY  - JOUR
AU  - Benouis, Mohamed
AU  - Andre, Elisabeth
AU  - Can, Yekta Said
PY  - 2024
DA  - 2024/12/23
TI  - Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy–Added Federated Learning Settings: Quantitative Study
JO  - JMIR Ment Health
SP  - e60003
VL  - 11
KW  - privacy preservation
KW  - multitask learning
KW  - federated learning
KW  - privacy
KW  - physiological signals
KW  - affective computing
KW  - wearable sensors
KW  - sensitive data
KW  - empathetic sensors
KW  - data privacy
KW  - digital mental health
KW  - wearables
KW  - ethics
KW  - emotional well-being
AB  - Background: The rise of wearable sensors marks a significant development in the era of affective computing. Their popularity is continuously increasing, and they have the potential to improve our understanding of human stress. A fundamental aspect within this domain is the ability to recognize perceived stress through these unobtrusive devices. Objective: This study aims to enhance the performance of emotion recognition using multitask learning (MTL), a technique extensively explored across various machine learning tasks, including affective computing. By leveraging the shared information among related tasks, we seek to augment the accuracy of emotion recognition while confronting the privacy threats inherent in the physiological data captured by these sensors. Methods: To address the privacy concerns associated with the sensitive data collected by wearable sensors, we proposed a novel framework that integrates differential privacy and federated learning approaches with MTL. This framework was designed to efficiently identify mental stress while preserving private identity information. Through this approach, we aimed to enhance the performance of emotion recognition tasks while preserving user privacy. Results: Comprehensive evaluations of our framework were conducted using 2 prominent public datasets. The results demonstrate a significant improvement in emotion recognition accuracy, achieving a rate of 90%. Furthermore, our approach effectively mitigates privacy risks, as evidenced by limiting reidentification accuracies to 47%. Conclusions: This study presents a promising approach to advancing emotion recognition capabilities while addressing privacy concerns in the context of empathetic sensors. By integrating MTL with differential privacy and federated learning, we have demonstrated the potential to achieve high levels of accuracy in emotion recognition while ensuring the protection of user privacy. This research contributes to the ongoing efforts to use affective computing in a privacy-aware and ethical manner. 
SN  - 2368-7959
UR  - https://mental.jmir.org/2024/1/e60003
UR  - https://doi.org/10.2196/60003
DO  - 10.2196/60003
ID  - info:doi/10.2196/60003
ER  -