梳理云端人脸隐私保护方法，帮用户防滥用
A Survey on Facial Image Privacy Preservation in Cloud-Based Services
- 分图像模糊和对抗扰动两类技术保护人脸隐私
- 对比分析了各类方法的防护效果与适用场景
- 适合关注云服务隐私安全的研究者与开发者
面部识别模型被商业企业、政府机构及云服务商广泛用于身份验证、消费者服务和监控。这些模型通常基于大量人脸数据训练，并在云端平台进行处理与存储，引发严重隐私担忧。用户的人脸图像可能在未经同意的情况下被滥用，导致数据泄露和非法使用。本文综述了当前云端服务中人脸图像隐私保护的主流方法，将其分为基于图像混淆的保护与基于对抗扰动的保护两大类。对两类方法进行了深入分析，提供定性与定量比较，评估其有效性。同时，指出尚未解决的挑战，并提出未来研究方向，以提升云计算环境中的隐私保护能力。
原文摘要 · Abstract (English)
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.
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