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Privacy-preserving biometric verification with outsourced correlation filter computation

机译：具有外包相关滤波器计算的隐私保留生物识别验证

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摘要

In traditional biometric verification systems, personal computer stores biometric database and performs verification process. Because of limited storage, capacity, and computational power, both cloud computing and data centers provide these facilities for users and enterprises. However, shifting from user-owned and user-operated system to public and untrusted access services have raised concerns over data security, either in storage or computation phases. In this work, we propose a framework for fully privacy-preserving biometric verification with outsourcing computational tasks to the commerical public cloudsl. Firstly, privacy of the data used for biometric verification is preserved by encrypting training images. Secondly, for protecting the privacy of the biometric verification model, all correlation filter computation and verification stage are performed over encrypted biometric images in server side. Finally, privacy of the biometric verification result is preserved by sending it to the client for further investigation. Our solution provides anonymous access, unlinkability, and the confidentiality of transmitted data. I will be shown that our scheme is secure in the semi-honest server and has it reaches accuracy of 93.7% on facial dataset and 92% on fingerprint dataset.
机译：在传统的生物识别系统中，个人计算机存储生物识别数据库并执行验证过程。由于存储，容量和计算能力有限，云计算和数据中心都为用户和企业提供了这些设施。然而，从用户拥有和用户操作的系统转移到公共和不受信任的访问服务已经提出了对数据安全性的关注，无论是在存储还是计算阶段。在这项工作中，我们提出了一个完全隐私保留生物识别的框架，并将计算任务保留为商业公共Coundsl。首先，通过加密培训图像保留用于生物识别验证的数据的隐私。其次，为了保护生物识别模型的隐私，所有相关滤波器计算和验证阶段都在服务器侧的加密生物识别图像上执行。最后，通过将其发送给客户进行进一步调查，保留生物识别验证结果的隐私。我们的解决方案提供了传输数据的匿名访问，可解释性和机密性。我将表明，我们的计划在半诚实服务器中是安全的，并且在面部数据集中达到93.7％的准确性，指纹数据集92％。

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