A Privacy-Preserving Blockchain and Machine Learning Framework for Secure Next-Generation Genomic Data Analysis

Abstract

Next-generation genomic data analysis faces ongoing challenges in collaboration, privacy, and scalability. With data protection and no access control deficiency, centralized systems lack sufficient protection for sensitive genomic data. The first-of-its-kind analysis of genomics combined with integrated machine learning and homomorphic encryption with the new privacy-preserving computational framework will be revolutionary. The use of smart contracts, which is unlike other frameworks, for access control, tokenized encrypted, and suspended federated model training during the suspension of other research nodes will be unprecedented. Simulation of genomic datasets (0 Major issues were identified in file sync performance and data protection and security) Vis-a-vis the other frameworks, the Model Proposed outperformed traditional, federated and HE based frameworks with significant. Of the models proposed, this one is the most impressive with a score of 94% precision, 92% recall, 0.93 computed F1 score, and 0.96 Area Under Curve (AUC). The model with the best performance. With 5K genomic datasets in a pilot simulation, collaboration improved by 25% with no breaches in the datasets. The promise of this design to ethically and securely address privacy-preserving genomic data analysis and the subsequent use of Artificial Intelligence in biomedical systems will be groundbreaking.