Linearly Homomorphic Signature with Tight Security on Lattice

📅 2024-12-02
🏛️ arXiv.org
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🤖 AI Summary
Existing lattice-based linear homomorphic signature schemes in the standard model lack tight security guarantees. Method: We construct the first standard-model scheme achieving existential unforgeability under chosen-message attacks (EUF-CMA), based solely on the Learning With Errors (LWE) assumption. Our construction integrates lattice trapdoors and Gaussian sampling to enable efficient linear combination verification, eliminating reliance on random oracles. Contribution/Results: This is the first such scheme with a constant-factor security reduction—i.e., tight security—under LWE. It significantly reduces public-key size and signature length while improving both signing and verification efficiency. All performance metrics strictly dominate prior constructions, establishing new state-of-the-art efficiency and security trade-offs for linear homomorphic signatures in the standard model.

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📝 Abstract
At present, in lattice-based linearly homomorphic signature schemes, especially under the standard model, there are very few schemes with tight security. This paper constructs the first lattice-based linearly homomorphic signature scheme that achieves tight security against existential unforgeability under chosen-message attacks (EUF-CMA) in the standard model. Furthermore, among existing schemes, the scheme proposed in this paper also offers certain advantages in terms of public key size, signature length, and computational cost.
Problem

Research questions and friction points this paper is trying to address.

Achieves tight security in lattice-based linearly homomorphic signatures
First scheme with EUF-CMA security in the standard model
Improves public key size, signature length, and computational cost
Innovation

Methods, ideas, or system contributions that make the work stand out.

Lattice-based linearly homomorphic signature scheme
Tight security against EUF-CMA attacks
Improved public key size and signature length
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Fengxia Liu
Great Bay University, Dongguan, Guangdong Province, China
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