{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/119518"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/119518","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A system for privacy-preserving machine learning on personal data","abstract":"This 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.","abstract_html":"This 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.","abstract_has_math":false,"creators":["Cyphers, Bennett James"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Kalyan Veeramachaneni."],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-22T22:22:29Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["A system for privacy-preserving machine learning on personal data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kalyan Veeramachaneni."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Cyphers, Bennett James"],"dc:date.accessioned":["2018-12-11T20:38:28Z"],"dc:date.available":["2018-12-11T20:38:28Z"],"dc:date.issued":["2017"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 81-85)."],"dc:description.abstract":["This 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. 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