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Practical Privacy-Preserving Federated Learning with Secure Multi-Party Computation

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dc.contributor.authorAkhtar, Benjamin Asaden
dc.contributor.committeechairXiong, Wenjieen
dc.contributor.committeememberShao, Linboen
dc.contributor.committeememberHoang, Thangen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2024-08-13T08:00:44Zen
dc.date.available2024-08-13T08:00:44Zen
dc.date.issued2024-08-12en
dc.description.abstractgeneralIn a world with ever greater need for machine learning and artificial intelligence, it has be- come increasingly important to offload computation intensive tasks to companies with the compute resources to perform training on potentially sensitive data. In applications such as finance or healthcare, the data providers may have a need to train large quantities of data, but cannot reveal the data to outside parties for legal or other reasons. Originally, using a decentralized training method known as Federated Learning (FL) was proposed to ensure data did not leave the client's device. This method still was susceptible to attacks and further security was needed. Multi-Party Computation (MPC) was proposed in conjunction with FL as it provides a way to securely compute with no leakage of data values. This was utilized in a framework called SAFEFL, however, it was extremely slow. Reducing the computation overhead using programming tools at our disposal for this frame- work turns it from a unpractical to useful design. The design can now be used in industry with some overhead compared to non-MPC computing, however, it has been greatly im- proved.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:41285en
dc.identifier.urihttps://hdl.handle.net/10919/120914en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectMulti-Party Computationen
dc.subjectMachine Learningen
dc.subjectFederated Learningen
dc.subjectDifferential Privacyen
dc.titlePractical Privacy-Preserving Federated Learning with Secure Multi-Party Computationen
dc.typeThesisen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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