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P2ESA: Privacy-Preserving Environmental Sensor-Based Authentication.

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Summary
This summary is machine-generated.

This study introduces P²ESA, a method using adversarial deep learning to protect sensitive user data from Internet of Things (IoT) devices during authentication. It ensures high identification accuracy while fully obscuring private information.

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behavioural authenticationedge computingprivacy-preserving learningubiquitous sensing

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Internet of Things (IoT) devices are increasingly prevalent in daily life.
  • IoT data can be used for user identification and authentication.
  • Collected data may inadvertently reveal sensitive personal information beyond authentication needs.

Purpose of the Study:

  • To develop a method for removing privacy-revealing information from IoT data.
  • To maintain high user authentication performance after privacy enhancement.
  • To address computational limitations of edge devices in machine learning pipelines.

Main Methods:

  • Utilized adversarial deep learning techniques for privacy information obfuscation.
  • Developed offloading techniques for computationally weak edge devices.
  • Implemented and trained the P²ESA system on multimodal IoT datasets.

Main Results:

  • Achieved user identification rates between 75.85% and 93.31%, significantly outperforming the 6.67% baseline.
  • Demonstrated efficient deployment and training of the P²ESA system.
  • Successfully obfuscated sensitive user information while preserving authentication utility.

Conclusions:

  • P²ESA offers a promising solution for secure authentication in IoT environments.
  • The method effectively balances user identification accuracy with robust privacy protection.
  • P²ESA addresses computational challenges for edge devices in machine learning applications.