Pura实现加密数据下高效人脸识别，速度提升16倍。
Pura: An Efficient Privacy-Preserving Solution for Face Recognition
- 基于门限帕特里亚密码构建非交互式隐私保护架构
- 在加密数据上直接完成人脸识别，速度比当前最优快16倍
- 适合需要高隐私保护的医疗、安防等场景
人脸识别能通过面部图像有效识别目标人物，但敏感面部图像引发隐私担忧。尽管隐私保护人脸识别是潜在解决方案，现有方法或无法充分保护隐私，或效率不足。为此，我们提出名为Pura的高效隐私保护人脸识别方案，可在加密数据上高效实现人脸识别并充分保护面部隐私。具体而言，通过门限帕特里亚密码设计非交互式隐私保护架构，并精心设计一系列底层安全计算协议，支持在加密数据上直接进行人脸识别操作。此外，引入并行计算机制以提升安全计算协议性能。隐私分析表明Pura能全面保护个人面部隐私。实验评估显示，Pura的识别速度相比当前最优方案最高提升16倍。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。