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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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PARS: Privacy-Aware Reward System for Mobile Crowdsensing Systems.

Zhong Zhang1, Dae Hyun Yum2, Minho Shin1

  • 1Department of Computer Engineering, Myongji University, Yongin 17058, Korea.

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This study introduces a privacy-preserving reward system for crowdsensing using blind signatures. It protects user privacy by separating task contributions from reward claims, encouraging wider participation.

Keywords:
aggregationblind signatureincentivemobile crowdsensingprivacy-preserving

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Area of Science:

  • Computer Science
  • Information Security

Background:

  • Crowdsensing systems leverage ubiquitous smartphones for wide-area data collection.
  • Incentive mechanisms are crucial for participant engagement but pose privacy risks.
  • Current systems may expose sensitive sensor data and link it to user identities during reward distribution.

Purpose of the Study:

  • To design and evaluate a novel privacy-preserving reward system for crowdsensing applications.
  • To address the inherent privacy risks associated with participant rewards in crowdsensing.
  • To enhance user trust and encourage broader participation in sensing tasks.

Main Methods:

  • Implementation of a reward system utilizing blind signature technology.
  • Decoupling the process of contribution verification from reward claim issuance.
  • Experimental validation of the proposed system's feasibility and efficiency.

Main Results:

  • The proposed blind signature-based scheme effectively protects participant privacy.
  • Contributions are decoupled from reward claims, preventing sensitive data linkage.
  • Experimental results confirm the mechanism's feasibility and computational efficiency.

Conclusions:

  • The developed privacy-preserving reward system enhances security in crowdsensing.
  • Blind signatures offer a viable solution for mitigating privacy concerns in crowdsensing rewards.
  • The system promotes greater participation by safeguarding user privacy and data.