Linearly Homomorphic Signature with Tight Security on Lattice
- URL: http://arxiv.org/abs/2412.01641v2
- Date: Tue, 03 Dec 2024 15:03:09 GMT
- Title: Linearly Homomorphic Signature with Tight Security on Lattice
- Authors: Heng Guo, Kun Tian, Fengxia Liu, Zhiyong Zheng,
- Abstract summary: 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.
- Score: 7.911831986965765
- License:
- 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.
Related papers
- GRIFFIN: Effective Token Alignment for Faster Speculative Decoding [52.905060461479856]
GRIFFIN is a framework that incorporates a token-alignable training strategy and a token-alignable draft model.
Experiments on LLaMA-series and Vicuna models demonstrate that GRIFFIN achieves an average acceptance length improvement of over 7% and a speedup ratio exceeding 8%.
arXiv Detail & Related papers (2025-02-16T07:06:00Z) - Deliberative Alignment: Reasoning Enables Safer Language Models [64.60765108418062]
We introduce Deliberative Alignment, a new paradigm that teaches the model safety specifications and trains it to explicitly recall and accurately reason over the specifications before answering.
We used this approach to align OpenAI's o-series models, and achieved highly precise adherence to OpenAI's safety policies, without requiring human-written chain-of-thoughts or answers.
arXiv Detail & Related papers (2024-12-20T21:00:11Z) - Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering [55.15192437680943]
Generative models lack rigorous statistical guarantees for their outputs.
We propose a sequential conformal prediction method producing prediction sets that satisfy a rigorous statistical guarantee.
This guarantee states that with high probability, the prediction sets contain at least one admissible (or valid) example.
arXiv Detail & Related papers (2024-10-02T15:26:52Z) - ZKFault: Fault attack analysis on zero-knowledge based post-quantum digital signature schemes [0.32248805768155825]
We show that we can recover the entire secret key of LESS and CROSS using as little as a single fault.
In this work, we first analyze the LESS signature scheme and devise our attack. Furthermore, we showed how this attack can be extended to the CROSS signature scheme.
arXiv Detail & Related papers (2024-09-11T09:54:45Z) - Generalized Quantum-assisted Digital Signature [2.187441808562386]
This paper introduces an improved version of a recently proposed scheme whose information theoretic security is inherited by adopting QKD keys for digital signature purposes.
Its security against forging is computed considering a trial-and-error approach taken by the malicious forger and GQaDS parameters are optimized via an analytical approach balancing between forgery and repudiation probabilities.
arXiv Detail & Related papers (2024-06-28T15:04:38Z) - Coding-Based Hybrid Post-Quantum Cryptosystem for Non-Uniform Information [53.85237314348328]
We introduce for non-uniform messages a novel hybrid universal network coding cryptosystem (NU-HUNCC)
We show that NU-HUNCC is information-theoretic individually secured against an eavesdropper with access to any subset of the links.
arXiv Detail & Related papers (2024-02-13T12:12:39Z) - Constructing a fully homomorphic encryption scheme with the Yoneda Lemma [0.0]
The paper redefines the foundations of asymmetric cryptography's homomorphic cryptosystems through the application of the Yoneda Lemma.
It demonstrates that widely adopted systems, including ElGamal, RSA, Benaloh, Regev's LWE, and NTRUEncrypt, are directly derived from the principles of the Yoneda Lemma.
This synthesis leads to the creation of a holistic homomorphic encryption framework, the Yoneda Encryption Scheme.
arXiv Detail & Related papers (2024-01-24T06:46:26Z) - In and Out-of-Domain Text Adversarial Robustness via Label Smoothing [64.66809713499576]
We study the adversarial robustness provided by various label smoothing strategies in foundational models for diverse NLP tasks.
Our experiments show that label smoothing significantly improves adversarial robustness in pre-trained models like BERT, against various popular attacks.
We also analyze the relationship between prediction confidence and robustness, showing that label smoothing reduces over-confident errors on adversarial examples.
arXiv Detail & Related papers (2022-12-20T14:06:50Z) - IDPS Signature Classification with a Reject Option and the Incorporation
of Expert Knowledge [3.867363075280544]
We propose and evaluate a machine learning signature classification model with a reject option (RO) to reduce the cost of setting up an intrusion detection and prevention system (IDPS)
To train the proposed model, it is essential to design features that are effective for signature classification.
The effectiveness of the proposed classification model is evaluated in experiments with two real datasets composed of signatures labeled by experts.
arXiv Detail & Related papers (2022-07-19T06:09:33Z) - Authentication Attacks on Projection-based Cancelable Biometric Schemes [0.6499759302108924]
Cancelable biometric schemes aim at generating secure biometric templates by combining user specific tokens, such as password, stored secret or salt, along with biometric data.
The security requirements of cancelable biometric schemes concern the irreversibility, unlinkability and revocability of templates, without losing in accuracy of comparison.
In this paper, we formalize these attacks for a traditional cancelable scheme with the help of integer linear programming (ILP) and quadratically constrained quadratic programming (QCQP)
arXiv Detail & Related papers (2021-10-28T14:39:35Z) - Stabilizing Equilibrium Models by Jacobian Regularization [151.78151873928027]
Deep equilibrium networks (DEQs) are a new class of models that eschews traditional depth in favor of finding the fixed point of a single nonlinear layer.
We propose a regularization scheme for DEQ models that explicitly regularizes the Jacobian of the fixed-point update equations to stabilize the learning of equilibrium models.
We show that this regularization adds only minimal computational cost, significantly stabilizes the fixed-point convergence in both forward and backward passes, and scales well to high-dimensional, realistic domains.
arXiv Detail & Related papers (2021-06-28T00:14:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.