Banov Marina, Pinčić Domagoj, Sušanj Diego, Lenac Kristijan
Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka, 51000, Croatia.
Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Radmile Matejčić 2, Rijeka, 51000, Croatia.
Sci Rep. 2025 Aug 12;15(1):29528. doi: 10.1038/s41598-025-14047-3.
Biometric identification systems offer strong security by relying on unique personal traits. At the same time, they raise significant privacy concerns because compromised biometric data cannot be revoked. This paper explores the use of homomorphic encryption (HE) as a means to protect biometric data during classification and reduce the risk of exposing sensitive information. Our system comprises a feature extractor which operates locally and a classifier which processes encrypted data. We demonstrate the feasibility of our approach on a gait recognition task, employing a vision transformer as a feature extractor and training several HE-compatible classifiers. Through a comprehensive statistical analysis, we evaluate the impact of HE on accuracy and computational complexity, especially with different activation functions and their polynomial approximations. Our results demonstrate the feasibility of secure and accurate gait recognition using HE, while highlighting the trade-off between security and performance.
生物识别系统依靠独特的个人特征提供强大的安全性。与此同时，它们引发了重大的隐私担忧，因为受损的生物识别数据无法撤销。本文探讨了使用同态加密（HE）作为在分类过程中保护生物识别数据并降低暴露敏感信息风险的一种手段。我们的系统包括一个在本地运行的特征提取器和一个处理加密数据的分类器。我们在步态识别任务上展示了我们方法的可行性，采用视觉Transformer作为特征提取器并训练了几个与HE兼容的分类器。通过全面的统计分析，我们评估了HE对准确性和计算复杂度的影响，特别是在不同激活函数及其多项式近似的情况下。我们的结果证明了使用HE进行安全准确的步态识别的可行性，同时突出了安全性和性能之间 的权衡。
