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

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.