A Survey on Facial Image Privacy Preservation in Cloud-Based Services
It addresses privacy concerns for users of cloud-based facial recognition services, but as a survey, it is incremental in summarizing existing work.
This survey tackles the problem of facial image privacy in cloud-based services by reviewing current methods, categorizing them into image obfuscation and adversarial perturbation approaches, and analyzing their effectiveness with qualitative and quantitative comparisons.
Facial recognition models are increasingly employed by commercial enterprises, government agencies, and cloud service providers for identity verification, consumer services, and surveillance. These models are often trained using vast amounts of facial data processed and stored in cloud-based platforms, raising significant privacy concerns. Users' facial images may be exploited without their consent, leading to potential data breaches and misuse. This survey presents a comprehensive review of current methods aimed at preserving facial image privacy in cloud-based services. We categorize these methods into two primary approaches: image obfuscation-based protection and adversarial perturbation-based protection. We provide an in-depth analysis of both categories, offering qualitative and quantitative comparisons of their effectiveness. Additionally, we highlight unresolved challenges and propose future research directions to improve privacy preservation in cloud computing environments.