Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions. Raw data streams from motion sensors embedded in portable and wearable devices may reveal private information about users without their awareness. For example, motion data might disclose the weight or gender of a user, or enable their re-identification. To address this problem, we propose an on-device transformation of sensor data to be shared for specific applications, such as monitoring selected daily activities, without revealing information that enables user identification. We formulate the anonymization problem using an information-theoretic approach and propose a new multi-objective loss function for training deep autoencoders. This loss function helps minimizing user-identity information as well as data distortion to preserve the application-specific utility. The training process regulates the encoder to disregard user-identifiable patterns and tunes the decoder to shape the output independently of users in the training set. The trained autoencoder can be deployed on a mobile or wearable device to anonymize sensor data even for users who are not included in the training dataset. Data from 24 users transformed by the proposed anonymizing autoencoder lead to a promising trade-off between utility and privacy, with an accuracy for activity recognition above 92% and an accuracy for user identification below 7%.
翻译:例如,运动数据可能披露用户的重量或性别,或允许其重新识别。为了解决这个问题,我们建议对传感器数据进行在线转换,供特定应用共享,例如监测选定的日常活动,而不披露能够识别用户身份的信息。我们使用信息理论方法来制定匿名化问题,并提议为深自动识别器培训开发新的多目标丢失功能。这一丢失功能有助于尽量减少用户身份信息以及数据扭曲,以维护具体应用程序的实用性。培训过程对编码器进行监管,以忽略用户身份模式,调整解码器,以独立决定培训数据集用户的输出。经过培训的自动解码器可以安装在移动或可磨损设备上,用于将即使在培训数据集上没有包含的用户也进行匿名化。从24个用户的加密数据转换到22个用户的准确性识别,为22个用户的可靠度,为22个用户的加密数据,为22个用户的准确度,为22个用户的准确度,为22个用户的准确度,为22个用户的准确度。