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Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
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Local differential privacy protection for wearable device data.

Zhangbing Li1, Baichuan Wang1, Jinsheng Li1

  • 1School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan, People's Republic of China.

Plos One
|August 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a privacy-preserving data collection method for wearable devices using local differential privacy. The technique protects user data while maintaining service usability, showing improved accuracy in heart rate data analysis.

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Area of Science:

  • Computer Science
  • Data Privacy
  • Wearable Technology

Background:

  • Wearable devices collect sensitive personal data, raising significant privacy concerns.
  • Existing data collection methods may compromise user privacy or service functionality.
  • Effective privacy protection is crucial for user trust and data utility in wearable services.

Purpose of the Study:

  • To propose a novel data collection scheme for wearable devices that enhances user privacy.
  • To ensure that privacy protection does not negatively impact the usability of wearable device services.
  • To address the challenge of protecting single-attribute numerical stream data collected by wearables.

Main Methods:

  • A local differential privacy (LDP) based scheme is developed for numerical stream data.
  • Salient data points are identified through censoring, and noise is adaptively added using the Laplacian mechanism based on a privacy budget.
  • Stream data curves are reconstructed and fitted to the noise-added salient points for privacy preservation.

Main Results:

  • The proposed scheme was evaluated using a heart rate dataset.
  • At a privacy budget of 0.5 (higher privacy), the mean relative error was 0.12.
  • This represents a 57.78% reduction in mean relative error compared to the scheme by Kim et al.

Conclusions:

  • The developed scheme effectively protects personal privacy in wearable device data.
  • The method improves the usability of mean value estimation for wearable stream data.
  • Balancing strong privacy protection with data utility is achievable for wearable sensor data.