Evaluating the Security of Merkle Trees in the Internet of Things: An Analysis of Data Falsification Probabilities
- URL: http://arxiv.org/abs/2404.12093v1
- Date: Thu, 18 Apr 2024 11:24:12 GMT
- Title: Evaluating the Security of Merkle Trees in the Internet of Things: An Analysis of Data Falsification Probabilities
- Authors: Oleksandr Kuznetsov, Alex Rusnak, Anton Yezhov, Kateryna Kuznetsova, Dzianis Kanonik, Oleksandr Domin,
- Abstract summary: This paper develops a theoretical framework to calculate the probability of data falsification, taking into account various scenarios based on the length of the Merkle path and hash length.
Empirical experiments validate the theoretical models, exploring simulations with diverse hash lengths and Merkle path lengths.
The findings reveal a decrease in falsification probability with increasing hash length and an inverse relationship with longer Merkle paths.
- Score: 27.541105686358378
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Addressing the critical challenge of ensuring data integrity in decentralized systems, this paper delves into the underexplored area of data falsification probabilities within Merkle Trees, which are pivotal in blockchain and Internet of Things (IoT) technologies. Despite their widespread use, a comprehensive understanding of the probabilistic aspects of data security in these structures remains a gap in current research. Our study aims to bridge this gap by developing a theoretical framework to calculate the probability of data falsification, taking into account various scenarios based on the length of the Merkle path and hash length. The research progresses from the derivation of an exact formula for falsification probability to an approximation suitable for cases with significantly large hash lengths. Empirical experiments validate the theoretical models, exploring simulations with diverse hash lengths and Merkle path lengths. The findings reveal a decrease in falsification probability with increasing hash length and an inverse relationship with longer Merkle paths. A numerical analysis quantifies the discrepancy between exact and approximate probabilities, underscoring the conditions for the effective application of the approximation. This work offers crucial insights into optimizing Merkle Tree structures for bolstering security in blockchain and IoT systems, achieving a balance between computational efficiency and data integrity.
Related papers
- Scalable Zero-Knowledge Proofs for Verifying Cryptographic Hashing in Blockchain Applications [16.72979347045808]
Zero-knowledge proofs (ZKPs) have emerged as a promising solution to address the scalability challenges in modern blockchain systems.
This study proposes a methodology for generating and verifying ZKPs to ensure the computational integrity of cryptographic hashing.
arXiv Detail & Related papers (2024-07-03T21:19:01Z) - Synthetic Tabular Data Validation: A Divergence-Based Approach [8.062368743143388]
Divergences quantify discrepancies between data distributions.
Traditional approaches calculate divergences independently for each feature.
We propose a novel approach that uses divergence estimation to overcome the limitations of marginal comparisons.
arXiv Detail & Related papers (2024-05-13T15:07:52Z) - Gaussian Mixture Models for Affordance Learning using Bayesian Networks [50.18477618198277]
Affordances are fundamental descriptors of relationships between actions, objects and effects.
This paper approaches the problem of an embodied agent exploring the world and learning these affordances autonomously from its sensory experiences.
arXiv Detail & Related papers (2024-02-08T22:05:45Z) - Toward Robust Uncertainty Estimation with Random Activation Functions [3.0586855806896045]
We propose a novel approach for uncertainty quantification via ensembles, called Random Activation Functions (RAFs) Ensemble.
RAFs Ensemble outperforms state-of-the-art ensemble uncertainty quantification methods on both synthetic and real-world datasets.
arXiv Detail & Related papers (2023-02-28T13:17:56Z) - Learning to Bound Counterfactual Inference in Structural Causal Models
from Observational and Randomised Data [64.96984404868411]
We derive a likelihood characterisation for the overall data that leads us to extend a previous EM-based algorithm.
The new algorithm learns to approximate the (unidentifiability) region of model parameters from such mixed data sources.
It delivers interval approximations to counterfactual results, which collapse to points in the identifiable case.
arXiv Detail & Related papers (2022-12-06T12:42:11Z) - Validation Diagnostics for SBI algorithms based on Normalizing Flows [55.41644538483948]
This work proposes easy to interpret validation diagnostics for multi-dimensional conditional (posterior) density estimators based on NF.
It also offers theoretical guarantees based on results of local consistency.
This work should help the design of better specified models or drive the development of novel SBI-algorithms.
arXiv Detail & Related papers (2022-11-17T15:48:06Z) - Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean
Estimation [8.9598796481325]
We investigate the consequences of this observation for both algorithms and computational complexity across different statistical problems.
We establish an information-computation gap for private sparse mean estimation.
We also give evidence for privacy-induced information-computation gaps for several other statistics and learning problems.
arXiv Detail & Related papers (2022-11-01T20:03:41Z) - Hybrid Bayesian network discovery with latent variables by scoring
multiple interventions [5.994412766684843]
We present the hybrid mFGS-BS (majority rule and Fast Greedy equivalence Search with Bayesian Scoring) algorithm for structure learning from discrete data.
The algorithm assumes causal insufficiency in the presence of latent variables and produces a Partial Ancestral Graph (PAG)
Experimental results show that mFGS-BS improves structure learning accuracy relative to the state-of-the-art and it is computationally efficient.
arXiv Detail & Related papers (2021-12-20T14:54:41Z) - PDC-Net+: Enhanced Probabilistic Dense Correspondence Network [161.76275845530964]
Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences.
We develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction.
Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets.
arXiv Detail & Related papers (2021-09-28T17:56:41Z) - Trust but Verify: Assigning Prediction Credibility by Counterfactual
Constrained Learning [123.3472310767721]
Prediction credibility measures are fundamental in statistics and machine learning.
These measures should account for the wide variety of models used in practice.
The framework developed in this work expresses the credibility as a risk-fit trade-off.
arXiv Detail & Related papers (2020-11-24T19:52:38Z) - General stochastic separation theorems with optimal bounds [68.8204255655161]
Phenomenon of separability was revealed and used in machine learning to correct errors of Artificial Intelligence (AI) systems and analyze AI instabilities.
Errors or clusters of errors can be separated from the rest of the data.
The ability to correct an AI system also opens up the possibility of an attack on it, and the high dimensionality induces vulnerabilities caused by the same separability.
arXiv Detail & Related papers (2020-10-11T13:12:41Z)
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.