Generalizable Features for Anonymizing Motion Signals Based on the Zeros of the Short-Time Fourier Transform

Pierre Rougé, Ali Moukadem, Alain Dieterlen, Antoine Boutet, Carole Frindel

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)89-99
Number of pages11
JournalJournal of Signal Processing Systems
Issue number1
Publication statusPublished - 2023 Jan
Externally publishedYes


  • Activity
  • Classification
  • Gaussian analytic functions
  • Machine learning
  • Privacy
  • Random forest
  • Time-frequency

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modelling and Simulation
  • Hardware and Architecture


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