论文标题
盲人：通过个性化联合学习，传感系统中的端到端隐私保护
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning
论文作者
论文摘要
本文提出了一个传感器数据匿名模型，该模型接受了分散数据的培训，并在数据实用程序和隐私之间进行了理想的权衡，即使在传感器数据具有不同基础分布的异质环境中也是如此。我们称为Blinder的匿名模型基于一个以对抗性方式训练的变异自动编码器和一个或多个歧视网络。我们使用模型不合时宜的元学习框架来调整通过联合学习训练的匿名模型，以适应每个用户的数据分布。我们在不同的设置下评估了Blinder，并表明它在两个IMU数据集上提供了端到端的隐私保护，而与对集中数据培训的最先进的匿名模型相比，将隐私损失提高高达4.00％，并将数据实用程序降低高达4.24％。我们还展示了Blinder匿名化射频传感方式的能力。我们的实验证实，Blinder可以一次掩盖多个私人属性，并且具有足够低的功耗和计算开销，以便将其部署在边缘设备和智能手机上，以执行传感器数据的实时匿名化。
This paper proposes a sensor data anonymization model that is trained on decentralized data and strikes a desirable trade-off between data utility and privacy, even in heterogeneous settings where the sensor data have different underlying distributions. Our anonymization model, dubbed Blinder, is based on a variational autoencoder and one or multiple discriminator networks trained in an adversarial fashion. We use the model-agnostic meta-learning framework to adapt the anonymization model trained via federated learning to each user's data distribution. We evaluate Blinder under different settings and show that it provides end-to-end privacy protection on two IMU datasets at the cost of increasing privacy loss by up to 4.00% and decreasing data utility by up to 4.24%, compared to the state-of-the-art anonymization model trained on centralized data. We also showcase Blinder's ability to anonymize the radio frequency sensing modality. Our experiments confirm that Blinder can obscure multiple private attributes at once, and has sufficiently low power consumption and computational overhead for it to be deployed on edge devices and smartphones to perform real-time anonymization of sensor data.
