面部数据最小化：浅层模型作为你的隐私过滤器
Facial Data Minimization: Shallow Model as Your Privacy Filter
AI总结：
针对人脸识别服务中面部数据泄露风险，提出基于浅层模型的隐私最小化变换（PMT）方法，生成混淆数据以保持授权模型性能并限制非授权模型，同时防御多种攻击，实验验证其有效性。
摘要：
人脸识别服务已被广泛应用于许多领域，给人们带来了极大的便利。然而，一旦用户的面部数据传输给服务提供商，用户将失去对其私人数据的控制。近年来，由于面部数据的泄露，存在各种安全和隐私问题。尽管已经提出了许多隐私保护方法，但当它们无法获知对手的策略或辅助数据时，这些方法通常会失效。因此，在本文中，通过充分考虑人脸识别服务系统中非常典型的上传面部图像和面部特征这两种情况，我们提出了一种数据隐私最小化变换（PMT）方法。该方法可以基于授权服务的浅层模型处理原始面部数据，以获得混淆数据。混淆数据不仅能保持授权模型上的满意性能并限制其他非授权模型上的性能，还能防止原始隐私数据通过人工智能方法和人类视觉窃取而泄露。此外，由于服务提供商可能对接收到的数据执行预处理操作，我们还提出了一种增强的扰动方法，以提高PMT的鲁棒性。另外，为了同时将一张面部图像授权给多个服务模型，提出了一种多重限制机制，以提高PMT的可扩展性。最后，我们进行了大量实验，评估了所提出的PMT在防御面部重建、数据滥用和面部属性估计攻击方面的有效性。这些实验结果表明，PMT在保持人脸识别准确性的同时，在防止面部数据滥用和隐私泄露方面表现良好。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
