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Privacy-preserving COVID-19 contact tracing solution based on blockchain.

Momeng Liu1, Zeyu Zhang1, Wenqiang Chai1

  • 1Shaanxi Key Laboratory of Clothing Intelligence, State and Local Joint Engineering Research Center for Advanced Networking and Intelligent Information Services, School of Computer Science, Xi'an Polytechnic University, Xi'an 710048, China.

Computer Standards & Interfaces
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Summary
This summary is machine-generated.

This study introduces a privacy-preserving blockchain solution for automated COVID-19 contact tracing apps. It uses advanced cryptography to protect user data while enabling efficient contact identification.

Keywords:
Aggregate SignatureBlockchainCOVID-19Privacy-preserving contact tracingZero-knowledge proof

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Public Health

Background:

  • The COVID-19 pandemic highlighted the need for efficient contact tracing.
  • Automated contact tracing apps offer speed but raise privacy concerns.
  • Manual tracing is costly and less efficient than automated methods.

Purpose of the Study:

  • To develop a privacy-preserving solution for automated contact tracing.
  • To leverage blockchain technology for enhanced security and efficiency.
  • To address the trade-off between tracing efficiency and user privacy.

Main Methods:

  • Utilized blockchain properties: anonymity, decentralization, and traceability.
  • Combined non-interactive zero-knowledge proofs with multi-signature technology.
  • Implemented public key aggregation for efficient signature verification.

Main Results:

  • Aggregating multiple signatures reduced the number of zero-knowledge proofs required.
  • The proposed solution saves storage and reduces verification time on the blockchain.
  • Experimental results confirmed full privacy protection with lower computational cost.

Conclusions:

  • The proposed blockchain-based solution effectively balances privacy and efficiency in contact tracing.
  • The cryptographic techniques enhance security and reduce computational overhead.
  • This approach offers a viable model for future public health surveillance systems.