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Kennesaw State University

Assessing Blockchain’s Potential to Ensure Data Integrity and Security for AI and Machine Learning Applications

Abstract

dc:description.abstract

<p>The increasing use of data-centric approaches in the fields of Machine Learning and Artificial Intelligence (ML/AI) has raised substantial issues over the security, integrity, and trustworthiness of data. In response to this challenge, Blockchain technology offered a promising and practical solution, as its inherent characteristics as a decentralized distributed ledger, coupled with cryptographic processes, offer an unprecedented level of data confidentiality and immutability. This study examines the mutually beneficial connection between Blockchain technology and ML/AI, using Blockchain's inherent capacity to protect against unauthorized alterations of data during the training phase of ML models. The method involves building valid blocks of data from the training dataset and then sending them to the mining process using smart contracts and the Proof of Work (PoW) consensus method. Using SHA256 to produce a cryptographic signature for each data block improves the aforementioned procedure. The public Ethereum blockchain serves as a secure repository for these signatures, whereas a cloud-based infrastructure houses the original data file. Particularly during the training phase of Machine Learning (ML) models, this cryptographic framework is critical in ensuring the data verification procedure. This research investigates the potential collaboration between Blockchain technology and ML/AI, bolstering data quality and trust to enhance data-driven decision-making fortifying the models' ability to provide precise and dependable results.</p>

Degree

thesis:*
Name thesis:degree_name
Master of Science in Information Technology (MSIT)
Level thesis:degree_level
Thesis
Discipline thesis:degree_discipline
Information Technology
Year dc:date.available
2023

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Siddika, Aiasha
Contributors dc:contributor
  • Dr. Liang Zhao
  • Dr. Seyedamin Pouriyeh
  • Dr. Xinyue Zhang

Subjects

dc:subject × 7

Identifiers

dc:identifier.*
Repository record dc:identifier
https://digitalcommons.kennesaw.edu/msit_etd/16
OAI identifier oai:identifier
oai:digitalcommons.kennesaw.edu:msit_etd-1019

Chain of custody

source
Harvested from
Kennesaw State University
Base URL
digitalcommons.kennesaw.edu/do/oai/
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Siddika, Aiasha. Assessing Blockchain’s Potential to Ensure Data Integrity and Security for AI and Machine Learning Applications. Thesis thesis, 2023. https://digitalcommons.kennesaw.edu/msit_etd/16