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.

翻译：本文提出一种传感器数据匿名化模型，该模型在去中心化数据上训练，即使在传感器数据具有不同底层分布的异构环境下，也能实现数据效用与隐私保护之间的理想平衡。该匿名化模型名为Blinder，基于变分自编码器与一个或多个以对抗方式训练的判别器网络。我们采用模型无关的元学习框架，将通过联邦学习训练的匿名化模型适配至每位用户的数据分布。我们在不同设置下评估Blinder，结果表明：与基于集中式数据训练的最新匿名化模型相比，该模型在两个IMU数据集上提供端到端隐私保护，同时隐私损失最多增加4.00%，数据效用最多降低4.24%。我们还展示了Blinder对射频传感模态的匿名化能力。实验证实，Blinder能够同时混淆多个隐私属性，且功耗和计算开销足够低，可部署于边缘设备和智能手机上，实现传感器数据的实时匿名化。