Welcome to the resource topic for 2025/2058
Title:
Real-Time Encrypted Emotion Recognition Using Homomorphic Encryption
Authors: Gyeongwon Cha, Dongjin Park, Yejin Choi, Eunji Park, Joon-Woo Lee
Abstract: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 frameworkâs 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.
ePrint: https://eprint.iacr.org/2025/2058
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