2026-09-18T19:29:04Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1328392025-10-30T17:03:45Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Lalana Kagal.
Kokje, Yashashree.
Massachusetts Institute of Technology. Engineering and Management Program.
System Design and Management Program.
Massachusetts Institute of Technology. Engineering and Management Program
2021-10-08T16:59:01Z
2021-10-08T16:59:01Z
2020
2020
https://hdl.handle.net/1721.1/132839
1262994244
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, May, 2020
Cataloged from the official version of thesis.
Includes bibliographical references (pages 57-59).
With the advent of machine learning, organizations today collect and process data at an unprecedented scale. This has led to rapid growth in innovation across industries, but also poses numerous challenges around maintaining user privacy. Specifically, in the field of healthcare and genomics where data is highly sensitive. Unlike credit cards or passwords, one's genomic information cannot be modified at will and has the ability to uniquely identify the individual. The objective of this thesis is to develop an easily configurable framework that would allow organizations to collaborate and advance genomic research without directly sharing user data with each other. This thesis includes the development of a privacy preserving framework for federated learning on genomic datasets that are distributed across organizational silos. PAGe (Privacy Aware Genomics) has been open-sourced and has a low barrier to entry. A packaged runtime environment is available that includes popular bioinformatics tools and machine learning libraries. Experimental setup is controlled through configuration files, allowing users to easily terminate, restart or reproduce results. Finally, there is an in depth evaluation of the framework using Type 2 Diabetes disease risk prediction as a case study with the 1000 genomes dataset as input.
by Yashashree Kokje.
S.M. in Engineering and Management
S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program
59 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Engineering and Management Program.
System Design and Management Program.
Privacy preserving framework for federated learning in genomics
Thesis
application/pdf
2021-10-08T16:59:01Z
Publication
Master
SysDes
unknown
unknown
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