Alam, Md. Golam RabiulRieyan, Sakib AnwarNews, Md. Raisul KabirRahman, A.B.M. MuntasirKhan, Sadia AfrinZaarif, Sultan Tasneem Jawad2023-08-302023-08-3020232023-03ID 19101024ID 19101058ID 19101042ID 19101034ID 19101206http://hdl.handle.net/10361/20203Cataloged from PDF version of thesis.Includes bibliographical references (pages 48-51).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.In this study, we present a novel approach for securely analyzing medical images using federated learning and partially homomorphic encryption within a distributed data fabric architecture. Our approach allows multiple parties to collaboratively train a machine learning model without exchanging raw data, while still maintaining compliance with laws and regulations such as HIPAA and GDPR. We demonstrate the effectiveness of our approach using pituitary tumor classification as a case study, achieving an overall accuracy of 83.31%. However, the primary focus of our work is on the development and evaluation of federated learning and partially homomorphic encryption as tools for secure medical image analysis. Our results show the potential for these techniques to be applied in other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning.51 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Data fabricFederated learningPartially homomorphic encryptionBig dataData integration (Computer science)Database managementThesis