Multiparty private summation protocol based on two-state quantum-mechanical system.

Area of Science:

• Quantum Information Science
• Cryptography
• Artificial Intelligence

Background:

• Increasing data sharing necessitates robust personal data protection.
• Quantum computing poses a threat to current encrypted data security.
• Existing quantum private summation (QPS) protocols have limitations, such as modulo 2 operation or reliance on high-dimensional qudits.

Purpose of the Study:

• To propose an innovative multiparty quantum private summation (QPS) method.
• To balance data sharing and privacy in collaborative AI model establishment.
• To offer a QPS solution that avoids high-dimensional qudits.

Main Methods:

• Development of a novel multiparty QPS protocol.
• Implementation of a method for secure information aggregation without disclosing individual data.
• Security analysis against common eavesdropping attacks.

Main Results:

• The proposed QPS protocol allows participants to share aggregated information securely.
• The method effectively protects individual data during the aggregation process.
• Security analysis confirms the protocol's resilience against eavesdropping.

Conclusions:

• The developed QPS method provides a practical solution for privacy-preserving AI model training.
• It enhances data security in distributed networks against quantum threats.
• The protocol ensures reliable personal data protection without complex qudit requirements.