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dc.contributor.advisorKalyan Veeramachaneni.en_US
dc.contributor.authorCyphers, Bennett Jamesen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2018-12-11T20:38:28Z
dc.date.available2018-12-11T20:38:28Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/119518
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 81-85).en_US
dc.description.abstractThis thesis describes the design and implementation of a system which allows users to generate machine learning models with their own data while preserving privacy. We approach the problem in two steps. First, we present a framework with which a user can collate personal data from a variety of sources in order to generate machine learning models for problems of the user's choosing. Second, we describe AnonML, a system which allows a group of users to share data privately in order to build models for classification. We analyze AnonML under differential privacy and test its performance on real-world datasets. In tandem, these two systems will help democratize machine learning, allowing people to make the most of their own data without relying on trusted third parties.en_US
dc.description.statementofresponsibilityby Bennett James Cyphers.en_US
dc.format.extent85 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleA system for privacy-preserving machine learning on personal dataen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc1066345066en_US


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