基于频率滤波编码器的隐私安全表示学习
Privacy Safe Representation Learning via Frequency Filtering Encoder
摘要：
深度学习模型越来越多地部署在现实世界的应用中。这些模型通常部署在服务器端，并以信息丰富的表示形式接收用户数据，以解决特定任务，例如图像分类。由于图像可能包含用户可能不愿意共享的敏感信息，因此隐私保护变得越来越重要。对抗表示学习（ARL）是训练在客户端运行并模糊图像的编码器的常用方法。假设模糊图像可以安全地传输并用于服务器上的任务，而没有隐私问题。然而，在这项工作中，我们发现训练重建攻击者可以成功恢复现有ARL方法的原始图像。为此，我们引入了一种新的ARL方法，通过低通滤波增强，限制可用的信息量在频域中进行编码。我们的实验结果表明，我们的方法经受重建攻击，同时优于以前的国家的最先进的方法的隐私效用权衡。我们进一步进行用户研究，以定性评估我们的重建攻击的防御。
英文摘要：
Deep learning models are increasingly deployed in real-world applications. These models are often deployed on the server-side and receive user data in an information-rich representation to solve a specific task, such as image classification. Since images can contain sensitive information, which users might not be willing to share, privacy protection becomes increasingly important. Adversarial Representation Learning (ARL) is a common approach to train an encoder that runs on the client-side and obfuscates an image. It is assumed, that the obfuscated image can safely be transmitted and used for the task on the server without privacy concerns. However, in this work, we find that training a reconstruction attacker can successfully recover the original image of existing ARL methods. To this end, we introduce a novel ARL method enhanced through low-pass filtering, limiting the available information amount to be encoded in the frequency domain. Our experimental results reveal that our approach withstands reconstruction attacks while outperforming previous state-of-the-art methods regarding the privacy-utility trade-off. We further conduct a user study to qualitatively assess our defense of the reconstruction attack.
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。
