Face recognition service has been used in many fields and brings much convenience to people. However, once the user's facial data is transmitted to a service provider, the user will lose control of his/her private data. In recent years, there exist various security and privacy issues due to the leakage of facial data. Although many privacy-preserving methods have been proposed, they usually fail when they are not accessible to adversaries' strategies or auxiliary data. Hence, in this paper, by fully considering two cases of uploading facial images and facial features, which are very typical in face recognition service systems, we proposed a data privacy minimization transformation (PMT) method. This method can process the original facial data based on the shallow model of authorized services to obtain the obfuscated data. The obfuscated data can not only maintain satisfactory performance on authorized models and restrict the performance on other unauthorized models but also prevent original privacy data from leaking by AI methods and human visual theft. Additionally, since a service provider may execute preprocessing operations on the received data, we also propose an enhanced perturbation method to improve the robustness of PMT. Besides, to authorize one facial image to multiple service models simultaneously, a multiple restriction mechanism is proposed to improve the scalability of PMT. Finally, we conduct extensive experiments and evaluate the effectiveness of the proposed PMT in defending against face reconstruction, data abuse, and face attribute estimation attacks. These experimental results demonstrate that PMT performs well in preventing facial data abuse and privacy leakage while maintaining face recognition accuracy.

翻译：面部识别服务已广泛应用于多个领域，为人们带来了极大便利。然而，一旦用户的面部数据传输至服务提供商，用户将失去对其隐私数据的控制。近年来，由于面部数据泄露，已经出现了多种安全与隐私问题。尽管已有多种隐私保护方法被提出，但当攻击者的策略或辅助数据不可获取时，这些方法通常失效。因此，本文充分考虑面部识别服务系统中两种典型场景（上传面部图像与面部特征），提出了一种数据隐私最小化变换方法。该方法基于授权服务的浅层模型对原始面部数据进行处理，生成混淆数据。混淆数据不仅能对授权模型保持令人满意的性能，同时限制其他未授权模型的性能，还能防止隐私原始数据被人工智能方法及人类视觉窃取。此外，考虑到服务提供商可能对接收数据执行预处理操作，我们进一步提出增强型扰动方法以提高PMT的鲁棒性。为解决单张面部图像同时授权给多个服务模型的问题，本文还提出多重约束机制以提升PMT的可扩展性。最后，我们通过大量实验评估了所提PMT方法在防御人脸重建、数据滥用及面部属性估计攻击中的有效性。实验结果表明，PMT在保持面部识别精度的同时，能有效防止面部数据滥用和隐私泄露。