Applications of Finite non-Abelian Simple Groups to Cryptography in the Quantum Era
- URL: http://arxiv.org/abs/2308.14725v1
- Date: Mon, 28 Aug 2023 17:30:00 GMT
- Title: Applications of Finite non-Abelian Simple Groups to Cryptography in the Quantum Era
- Authors: María Isabel González Vasco, Delaram Kahrobaei, Eilidh McKemmie,
- Abstract summary: We review some applications of finite non-abelian simple groups to cryptography and discuss different scenarios in which this theory is clearly central.
We look at constructions based on various group-theoretic factorization problems, review group theoretical hash functions, and discuss fully homomorphic encryption using simple groups.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The theory of finite simple groups is a (rather unexplored) area likely to provide interesting computational problems and modelling tools useful in a cryptographic context. In this note, we review some applications of finite non-abelian simple groups to cryptography and discuss different scenarios in which this theory is clearly central, providing the relevant definitions to make the material accessible to both cryptographers and group theorists, in the hope of stimulating further interaction between these two (non-disjoint) communities. In particular, we look at constructions based on various group-theoretic factorization problems, review group theoretical hash functions, and discuss fully homomorphic encryption using simple groups. The Hidden Subgroup Problem is also briefly discussed in this context.
Related papers
- Contracting Self-similar Groups in Group-Based Cryptography [0.0]
We propose self-similar contracting groups as a platform for cryptographic schemes based on simultaneous conjugacy search problem (SCSP)
The class of these groups contains extraordinary examples like Grigorchuk group, which is known to be non-linear.
We discuss benefits and drawbacks of using these groups in group-based cryptography and provide computational analysis of variants of the length-based attack on SCSP.
arXiv Detail & Related papers (2024-08-26T15:30:11Z) - Learning to be Simple [0.0]
We employ machine learning to understand structured mathematical data involving finite groups.
We derive a theorem about necessary properties of generators of finite simple groups.
Our work highlights the possibility of generating new conjectures and theorems in mathematics with the aid of machine learning.
arXiv Detail & Related papers (2023-12-08T19:00:00Z) - Subsets of groups in public-key cryptography [0.46960837342692324]
We present the subset version of two protocols introduced by Shpilrain and Ushakov with some examples in ascending HNN-extensions of free-abelian groups.
We also introduce several new group theoretic problems arising from this work.
arXiv Detail & Related papers (2023-11-25T14:35:36Z) - Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic
Segmentation [59.37587762543934]
This paper studies the problem of weakly open-vocabulary semantic segmentation (WOVSS)
Existing methods suffer from a granularity inconsistency regarding the usage of group tokens.
We propose the prototypical guidance network (PGSeg) that incorporates multi-modal regularization.
arXiv Detail & Related papers (2023-10-29T13:18:00Z) - Higher-order topological kernels via quantum computation [68.8204255655161]
Topological data analysis (TDA) has emerged as a powerful tool for extracting meaningful insights from complex data.
We propose a quantum approach to defining Betti kernels, which is based on constructing Betti curves with increasing order.
arXiv Detail & Related papers (2023-07-14T14:48:52Z) - Applying language models to algebraic topology: generating simplicial
cycles using multi-labeling in Wu's formula [0.0]
We take a step towards the goal of comprehending the group-theoretic structure of the generators of homotopy groups by leveraging the power of machine learning.
We present and evaluate language modelling approaches that employ multi-label information for input sequences, along with the necessary group-theoretic toolkit and non-neural baselines.
arXiv Detail & Related papers (2023-06-01T12:23:14Z) - Group conditional validity via multi-group learning [5.797821810358083]
We consider the problem of distribution-free conformal prediction and the criterion of group conditional validity.
Existing methods achieve such guarantees under either restrictive grouping structure or distributional assumptions.
We propose a simple reduction to the problem of achieving validity guarantees for individual populations by leveraging algorithms for a problem called multi-group learning.
arXiv Detail & Related papers (2023-03-07T15:51:03Z) - Equivariant Transduction through Invariant Alignment [71.45263447328374]
We introduce a novel group-equivariant architecture that incorporates a group-in hard alignment mechanism.
We find that our network's structure allows it to develop stronger equivariant properties than existing group-equivariant approaches.
We additionally find that it outperforms previous group-equivariant networks empirically on the SCAN task.
arXiv Detail & Related papers (2022-09-22T11:19:45Z) - Towards Group Robustness in the presence of Partial Group Labels [61.33713547766866]
spurious correlations between input samples and the target labels wrongly direct the neural network predictions.
We propose an algorithm that optimize for the worst-off group assignments from a constraint set.
We show improvements in the minority group's performance while preserving overall aggregate accuracy across groups.
arXiv Detail & Related papers (2022-01-10T22:04:48Z) - Differentially-Private Clustering of Easy Instances [67.04951703461657]
In differentially private clustering, the goal is to identify $k$ cluster centers without disclosing information on individual data points.
We provide implementable differentially private clustering algorithms that provide utility when the data is "easy"
We propose a framework that allows us to apply non-private clustering algorithms to the easy instances and privately combine the results.
arXiv Detail & Related papers (2021-12-29T08:13:56Z) - Lattice-Based Methods Surpass Sum-of-Squares in Clustering [98.46302040220395]
Clustering is a fundamental primitive in unsupervised learning.
Recent work has established lower bounds against the class of low-degree methods.
We show that, perhaps surprisingly, this particular clustering model textitdoes not exhibit a statistical-to-computational gap.
arXiv Detail & Related papers (2021-12-07T18:50:17Z)
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.