Data Sharing and Retrieval of Manufacturing Processes

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dc.contributor.authorSeth, Avien
dc.contributor.committeechairLourentzou, Isminien
dc.contributor.committeememberJin, Ranen
dc.contributor.committeememberJia, Ruoxien
dc.contributor.committeememberKarpatne, Anujen
dc.contributor.departmentComputer Science and Applicationsen
dc.date.accessioned2023-03-29T08:00:18Zen
dc.date.available2023-03-29T08:00:18Zen
dc.date.issued2023-03-28en
dc.description.abstractWith Industrial Internet, businesses can pool their resources to acquire large amounts of data that can then be used in machine learning tasks. Despite the potential to speed up training and deployment and improve decision-making through data-sharing, rising privacy concerns are slowing the spread of such technologies. As businesses are naturally protective of their data, this poses a barrier to interoperability. While previous research has focused on privacy-preserving methods, existing works typically consider data that is averaged or randomly sampled by all contributors rather than selecting data that are best suited for a specific downstream learning task. In response to the dearth of efficient data-sharing methods for diverse machine learning tasks in the Industrial Internet, this work presents an end-to end working demonstration of a search engine prototype built on PriED, a task-driven data-sharing approach that enhances the performance of supervised learning by judiciously fusing shared and local participant data.en
dc.description.abstractgeneralMy work focuses on PriED - a data sharing framework that enhances machine learning performance while also preserving user data privacy. In particular, I have built a working demonstration of a search engine that leverages the PriED framework and allows users to collaborate with their data without compromising their data privacy.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:36615en
dc.identifier.urihttp://hdl.handle.net/10919/114218en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectdata sharingen
dc.subjectprivacyen
dc.subjectcollaborationen
dc.subjectattentionen
dc.subjectdata distillationen
dc.titleData Sharing and Retrieval of Manufacturing Processesen
dc.typeThesisen
thesis.degree.disciplineComputer Science & Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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