A system for privacy-preserving machine learning on personal data
Author(s)Cyphers, Bennett James
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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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.
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).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.