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Generalizable Features for Anonymizing Motion Signals Based on the Zeros of the Short-Time Fourier Transform
Journal of Signal Processing Systems ( IF 1.8 ) Pub Date : 2022-07-25 , DOI: 10.1007/s11265-022-01798-9
Pierre Rougé, Ali Moukadem, Alain Dieterlen, Antoine Boutet, Carole Frindel

Thanks to the recent development of sensors and Internet of Things (IoT), it is now common to use mobile application to monitor health status. These applications rely on sensors embedded in the smartphones that measure several physical quantities such as acceleration or angular velocity. However, these data are private information that can be used to infer sensitive attributes. This paper presents a new approach to anonymize the motion sensor data, preventing the re-identification of the user based on a selection of handcrafted features extracted from the distribution of zeros of the Shot-Time Fourier Transform (STFT). This work is motivated by recent works which highlight the importance of the zeros of the STFT Flandrin (IEEE Processing Letters 22:2137-2141, 1) and link them in the case of white noise to Gaussian Analytical Functions (GAF) Bardenet et al. (Applied and Computational Harmonic Analysis 48:682-705, 2) where the distribution of their zeros is formally described. The proposed approach is compared with an extension of an earlier work based on filtering in the time-frequency plane and doing the classification task based on convolutional neural networks, for which we improved the evaluation method and investigated the benefits of gyroscopic sensor’s data. An extensive comparison is performed on a first public dataset to assess the accuracy of activity recognition and user re-identification. We showed not only that the proposed method gives better results in term of activity/identity recognition trade-off compared with the state of the art but also that it can be generalized to other datasets.



中文翻译:

基于短时傅里叶变换的零点对运动信号进行匿名化的可推广特征

由于传感器和物联网 (IoT) 的最新发展,现在使用移动应用程序来监测健康状况已经很普遍。这些应用依赖于嵌入在智能手机中的传感器,这些传感器可以测量加速度或角速度等多个物理量。但是,这些数据是可用于推断敏感属性的私人信息。本文提出了一种对运动传感器数据进行匿名化的新方法,防止基于从镜头时间傅里叶变换 (STFT) 的零点分布中提取的手工特征选择来重新识别用户。这项工作的动机是最近的工作突出了 STFT Flandrin 零点的重要性(IEEE Processing Letters 22:2137-2141,1) 并将它们在白噪声的情况下与高斯分析函数 (GAF) Bardenet 等人联系起来。(Applied and Computational Harmonic Analysis 48:682-705, 2) 其中零点的分布被正式描述。将所提出的方法与基于时频平面过滤和基于卷积神经网络的分类任务的早期工作的扩展进行了比较,我们改进了评估方法并研究了陀螺传感器数据的好处。对第一个公共数据集进行了广泛的比较,以评估活动识别和用户重新识别的准确性。我们表明,与现有技术相比,所提出的方法不仅在活动/身份识别权衡方面给出了更好的结果,而且它还可以推广到其他数据集。

更新日期:2022-07-26
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