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Real-Time Encrypted Emotion Recognition Using Homomorphic Encryption
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AI
DOI：10.1145/3770633.png)
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Emotion recognition has been an actively researched topic in the field of HCI. However, multimodal datasets used for emotion recognition often contain sensitive personal information, such as physiological signals, facial images, and behavioral patterns, raising significant privacy concerns. In particular, the privacy issues become crucial in workplace settings because of the risks such as surveillance and unauthorized data usage caused by the misuse of collected datasets. To address this issue, we propose an Encrypted Emotion Recognition (EER) framework that performs real-time inference on encrypted data using the CKKS homomorphic encryption (HE) scheme. We evaluated the proposed framework using publicly available WESAD and Hide-and-seek datasets, demonstrating successful stress/emotion recognition under encryption. The results demonstrated that encrypted inference achieved similar accuracy to plaintext inference, with accuracy of 0.966 (plaintext) vs. 0.967 (ciphertext) on the WESAD dataset, and 0.868 for both cases on the Hide-and-Seek dataset. Encrypted inference was performed on a GPU, with average inference times of 333 milliseconds for the general model and 455 milliseconds for the personalized model. Furthermore, we validated the feasibility of semi-supervised learning and model personalization in encrypted environments, enhancing the framework's real-world applicability. Our findings suggest that the EER framework provides a scalable, privacy-preserving solution for emotion recognition in domains such as healthcare and workplace settings, where securing sensitive data is of critical importance.
Keyword:
Data privacy
Emotion recognition
Cloud privacy
Privacy-preserving machine learning
Homomorphic encryption
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