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DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks

Abstract : With the widespread adoption of the quantified self movement, an increasing number of users rely on mobile applications to monitor their physical activity through their smartphones. Granting to applications a direct access to sensor data expose users to privacy risks. Indeed, usually these motion sensor data are transmitted to analytics applications hosted on the cloud leveraging machine learning models to provide feedback on their health to users. However, nothing prevents the service provider to infer private and sensitive information about a user such as health or demographic attributes. In this paper, we present DySan, a privacy-preserving framework to sanitize motion sensor data against unwanted sensitive inferences (i.e., improving privacy) while limiting the loss of accuracy on the physical activity monitoring (i.e., maintaining data utility). To ensure a good trade-off between utility and privacy, DySan leverages on the framework of Generative Adversarial Network (GAN) to sanitize the sensor data. More precisely, by learning in a competitive manner several networks, DySan is able to build models that sanitize motion data against inferences on a specified sensitive attribute (e.g., gender) while maintaining a high accuracy on activity recognition. In addition, DySan dynamically selects the sanitizing model which maximize the privacy according to the incoming data. Experiments conducted on real datasets demonstrate that DySan can drastically limit the gender inference to 47% while only reducing the accuracy of activity recognition by 3% (from 95% with raw data to 92% with sanitized data).
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Contributor : Antoine Boutet Connect in order to contact the contributor
Submitted on : Tuesday, October 20, 2020 - 11:44:13 AM
Last modification on : Monday, October 26, 2020 - 10:42:54 AM


  • HAL Id : hal-02512640, version 2
  • ARXIV : 2003.10325


Rosin Claude Ngueveu, Antoine Boutet, Carole Frindel, Sébastien Gambs, Théo Jourdan, et al.. DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks. [Research Report] RR-9325, inria. 2020, pp.27. ⟨hal-02512640v2⟩



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