Abstract
Face recognition is an effective technology for identifying a target person by facial images. However, sensitive facial images raises privacy concerns. Although privacy-preserving face recognition is one of potential solutions, this solution neither fully addresses the privacy concerns nor is efficient enough. To this end, we propose an efficient privacy-preserving solution for face recognition, named Pura, which sufficiently protects facial privacy and supports face recognition over encrypted data efficiently. Specifically, we propose a privacy-preserving and non-interactive architecture for face recognition through the threshold Paillier cryptosystem. Additionally, we carefully design a suite of underlying secure computing protocols to enable efficient operations of face recognition over encrypted data directly. Furthermore, we introduce a parallel computing mechanism to enhance the performance of the proposed secure computing protocols. Privacy analysis demonstrates that Pura fully safeguards personal facial privacy. Experimental evaluations demonstrate that Pura achieves recognition speeds up to 16 times faster than the state-of-the-art.
Abstract (translated)
面部识别技术通过面部图像来识别人脸，是一种有效的方法。然而，敏感的面部图像是隐私保护的一大隐患。虽然隐私保护型面部识别技术是潜在解决方案之一，但它们既未能充分解决隐私问题，又不够高效。为此，我们提出了一种高效的隐私保护方案Pura，该方案能够有效地保护面部隐私，并支持在加密数据上进行有效的面部识别操作。 具体来说，通过门限Paillier密码系统，我们设计了一个私密性保护且无需交互的面部识别架构。此外，为了直接对加密后的数据执行面部识别操作，我们精心设计了一套底层的安全计算协议。并且，我们还引入了并行计算机制以增强所提出的安全计算协议的性能。 隐私分析显示Pura能够全面保障个人面部信息的安全性。实验评估表明，与现有的前沿技术相比，Pura在识别速度方面最多可快达16倍。
URL
https://arxiv.org/abs/2505.15476
