Formalizing the Cryptographic Migration Problem

• URL: http://arxiv.org/abs/2408.05997v2
• Date: Wed, 4 Sep 2024 11:24:39 GMT
• Title: Formalizing the Cryptographic Migration Problem
• Authors: Daniel Loebenberger, Stefan-Lukas Gazdag, Daniel Herzinger, Eduard Hirsch, Christian Näther, Jan-Philipp Steghöfer,
• Abstract summary: transitioning to post-quantum cryptography is becoming increasingly critical to maintain the security of modern systems. This paper introduces a formal definition of the cryptographic migration problem and explores its complexities using a suitable directed graph model.
• Score: 2.4739484546803334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the advancements in quantum computing, transitioning to post-quantum cryptography is becoming increasingly critical to maintain the security of modern systems. This paper introduces a formal definition of the cryptographic migration problem and explores its complexities using a suitable directed graph model. Characteristics of the resulting migration graphs are analyzed and trade-offs discussed. By using classical mathematical results from combinatorics, probability theory and combinatorial analysis, we assess the challenges of migrating ``random'' large cryptographic IT-infrastructures. We show that any sufficiently large migration project that follows our model has an intrinsic complexity, either due to many dependent (comparatively easy) migration steps or due to at least one complicated migration step. This proves that in a suitable sense cryptographic migration is hard in general. Furthermore, we analyze the proposed model with respect to practical applicability and explain the difficulties that emerge when we try to model real-world migration projects.

Related papers

• MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities [72.68829963458408]
  We present MergeNet, which learns to bridge the gap of parameter spaces of heterogeneous models. The core mechanism of MergeNet lies in the parameter adapter, which operates by querying the source model's low-rank parameters. MergeNet is learned alongside both models, allowing our framework to dynamically transfer and adapt knowledge relevant to the current stage.
  arXiv  Detail & Related papers  (2024-04-20T08:34:39Z)
• Migrating Software Systems towards Post-Quantum-Cryptography -- A Systematic Literature Review [2.4739484546803334]
  A migration to post-quantum-cryptography (PQC) is necessary for networks and their components. Recent standardization efforts already push quantum-safe networking forward. However, the literature is still not in consensus about definitions and best practices.
  arXiv  Detail & Related papers  (2024-04-19T12:43:32Z)
• Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models [102.72940700598055]
  In reasoning tasks, even a minor error can cascade into inaccurate results. We develop a method that avoids introducing external resources, relying instead on perturbations to the input. Our training approach randomly masks certain tokens within the chain of thought, a technique we found to be particularly effective for reasoning tasks.
  arXiv  Detail & Related papers  (2024-03-04T16:21:54Z)
• It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation [50.06412862964449]
  Attack Graph (AG) represents the best-suited solution to support cyber risk assessment for multi-step attacks on computer networks. Current solutions propose to address the generation problem from the algorithmic perspective and postulate the analysis only after the generation is complete. This paper rethinks the classic AG analysis through a novel workflow in which the analyst can query the system anytime.
  arXiv  Detail & Related papers  (2023-12-27T10:44:58Z)
• A Framework for Migrating to Post-Quantum Cryptography: Security Dependency Analysis and Case Studies [3.890207460112498]
  cryptography, once deemed secure for decades, are now at risk of being compromised. There is an urgent need to migrate to quantum-resistant cryptographic systems. We present a comprehensive framework designed to assist enterprises with this transition.
  arXiv  Detail & Related papers  (2023-07-13T01:51:15Z)
• Faith and Fate: Limits of Transformers on Compositionality [109.79516190693415]
  We investigate the limits of transformer large language models across three representative compositional tasks. These tasks require breaking problems down into sub-steps and synthesizing these steps into a precise answer. Our empirical findings suggest that transformer LLMs solve compositional tasks by reducing multi-step compositional reasoning into linearized subgraph matching.
  arXiv  Detail & Related papers  (2023-05-29T23:24:14Z)
• Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior [1.2891210250935143]
  We derive an analytically tractable Bayesian method that uses repeated interactions and a topological prior to infer transition matrices data-efficiently. We show that it recovers the transition probabilities with higher accuracy and that it is robust even in cases when the knowledge of the topological constraint is partial.
  arXiv  Detail & Related papers  (2022-10-27T13:17:47Z)
• Investigating internal migration with network analysis and latent space representations: An application to Turkey [0.0]
  We provide an in-depth investigation into the structure and dynamics of the internal migration in Turkey from 2008 to 2020. We identify a set of classical migration laws and examine them via various methods for signed network analysis, ego network analysis, representation learning, temporal stability analysis, and network visualization. The findings show that, in line with the classical migration laws, most migration links are geographically bounded with several exceptions involving cities with large economic activity.
  arXiv  Detail & Related papers  (2022-01-10T18:58:02Z)
• Bayesian Inductive Learner for Graph Resiliency under uncertainty [1.9254132307399257]
  We propose a Bayesian graph neural network-based framework for identifying critical nodes in a large graph. The fidelity and the gain in computational complexity offered by the framework are illustrated.
  arXiv  Detail & Related papers  (2020-12-26T07:22:29Z)
• Multilayer Clustered Graph Learning [66.94201299553336]
  We use contrastive loss as a data fidelity term, in order to properly aggregate the observed layers into a representative graph. Experiments show that our method leads to a clustered clusters w.r.t. We learn a clustering algorithm for solving clustering problems.
  arXiv  Detail & Related papers  (2020-10-29T09:58:02Z)
• Adversarial Attack on Community Detection by Hiding Individuals [68.76889102470203]
  We focus on black-box attack and aim to hide targeted individuals from the detection of deep graph community detection models. We propose an iterative learning framework that takes turns to update two modules: one working as the constrained graph generator and the other as the surrogate community detection model.
  arXiv  Detail & Related papers  (2020-01-22T09:50:04Z)

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.