Secure computation protocol of Chebyshev distance under the malicious model.

Area of Science:

• Cryptography
• Computer Science
• Data Security

Background:

• Secure multi-party computation (SMC) is vital for confidential data analysis.
• Chebyshev distance computation is essential for similarity, classification, and clustering in electronic archives.
• Existing methods lack robust security for sensitive archival data.

Purpose of the Study:

• To propose secure protocols for Chebyshev distance computation.
• To enhance data security in electronic archival management systems.
• To ensure privacy during sensitive data queries and sharing.

Main Methods:

• Developed a secure protocol for Chebyshev distance under a semi-honest model using NTRU cryptosystem and vector encoding.
• Transformed Chebyshev distance computation into an inner product of confidential vectors.
• Introduced a secure protocol for malicious model scenarios using digital commitments and mutual decryption.

Main Results:

• Validated security of the semi-honest protocol using the model paradigm.
• Affirmed security of the malicious protocol using the real/ideal model paradigm.
• Demonstrated efficiency and practical applicability through theoretical analysis and simulations.

Conclusions:

• The proposed protocols offer efficient and secure Chebyshev distance computation for electronic archives.
• These methods significantly enhance the security of sensitive archival information.
• The protocols are validated for both semi-honest and malicious adversarial models.