PEEP with Cloud Encryption: A Dual-Layered Privacy Solution for Emotion Recognition Systems

Recent advancements in image-based Facial emotion recognition systems offer deep insights into human psychological states, holding promise for valuable applications such as measuring customer satisfaction in public service areas or student engagement in classrooms. However, the potential impacts on the privacy of individual users of these systems cannot be ignored. In our study, we present PEEP (Privacy using EigEnface Perturbation) integrated with cloud storage encryption as a dual-layered approach to address privacy concerns in such systems. PEEP processes facial data by extracting eigenfaces and adding specific noise, ensuring the anonymity of identities while retaining the capability to recognize emotions. In tandem, cloud storage encryption guarantees that data, if intercepted during transmission or storage, stays encrypted and secure. This combined strategy offers an enhanced privacy solution for emotion recognition systems on remote servers. This study aligns with international initiatives to promote the responsible development and application of artificial intelligence, while emphasizing the importance of upholding human ethical standards and security.