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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Siddika, Aiasha"],"institution":null,"degree_name":"Master of Science in Information Technology (MSIT)","degree_level":"Thesis","degree_discipline":"Information Technology","degree_department":null,"school":null,"contributors":["Dr. Liang Zhao","Dr. Seyedamin Pouriyeh","Dr. Xinyue Zhang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12-01T08:00:00Z","date_published":"2023-12-01T08:00:00Z","updated_at":"2026-07-24T02:44:06Z","subjects":["Blockchain","Data Integrity","Consensus Mechanism","Proof of Work (PoW)","SHA256","Data-driven Decision Making","Computer and Systems Architecture"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.kennesaw.edu/msit_etd/16","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Liang Zhao","Dr. Seyedamin Pouriyeh","Dr. Xinyue Zhang"]},{"key":"dc:creator","label":"Author","values":["Siddika, Aiasha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-12-08T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Information Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Information Technology (MSIT)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Blockchain","Data Integrity","Consensus Mechanism","Proof of Work (PoW)","SHA256","Data-driven Decision Making","Computer and Systems Architecture"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.kennesaw.edu/msit_etd/16"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Assessing Blockchain’s Potential to Ensure Data Integrity and Security for AI and Machine Learning Applications"]}]}],"canonical_facts":{"dc:contributor":["Dr. Liang Zhao","Dr. Seyedamin Pouriyeh","Dr. Xinyue Zhang"],"dc:creator":["Siddika, Aiasha"],"dc:date.available":["2023-12-08T08:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://digitalcommons.kennesaw.edu/msit_etd/16"],"dc:subject":["Blockchain","Data Integrity","Consensus Mechanism","Proof of Work (PoW)","SHA256","Data-driven Decision Making","Computer and Systems Architecture"],"dc:title":["Assessing Blockchain’s Potential to Ensure Data Integrity and Security for AI and Machine Learning Applications"],"thesis:degree_discipline":["Information Technology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Information Technology (MSIT)"]},"updated_at":"2026-07-24T02:44:06Z"}