A secure and efficient certificateless content extraction signature with privacy protection

Area of Science:

• Cryptography
• Information Security
• Computer Science

Background:

• Digital signatures are crucial for data integrity and authenticity.
• Existing certificateless public key cryptosystems often face efficiency challenges, particularly with bilinear pairings.
• Privacy protection in signature schemes is increasingly important for sensitive data.

Purpose of the Study:

• To propose a novel Secure and Efficient Certificateless Content Extraction Signature with Privacy Protection (SECCESPP).
• To enhance the efficiency and privacy of content extraction signatures.
• To provide a robust security and privacy framework for digital signatures.

Main Methods:

• Utilized scalar multiplication of elliptic curves to replace inefficient bilinear pairings.
• Incorporated the signcryption technique for privacy protection of signed messages.
• Demonstrated correctness through message consistency and equation accuracy.
• Analyzed security and privacy using the elliptic curve discrete logarithm problem in the random oracle model and the ProVerif tool.

Main Results:

• The SECCESPP scheme demonstrates correctness via consistent messages and accurate equations.
• Security and privacy analyses confirm the scheme's robustness.
• Theoretical and experimental results show SECCESPP outperforms existing schemes in efficiency.

Conclusions:

• The proposed SECCESPP scheme effectively balances efficiency and privacy in content extraction signatures.
• SECCESPP offers a viable solution for secure and private digital signing.
• The scheme's reliance on elliptic curve cryptography provides a strong security foundation.