Tiresias : a peer-to-peer platform for privacy preserving machine learning
Author(s)Zhang, Kevin,M. Eng.Massachusetts Institute of Technology.
Peer-to-peer platform for privacy preserving machine learning
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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Big technology firms have a monopoly over user data. To remediate this, we propose a data science platform which allows users to collect their personal data and offer computations on them in a differentially private manner. This platform provides a mechanism for contributors to offer computations on their data in a privacy-preserving way and for requesters -- i.e. anyone who can benefit from applying machine learning to the users' data -- to request computations on user data they would otherwise not be able to collect. Through carefully designed differential privacy mechanisms, we can create a platform which gives people control over their data and enables new types of applications.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020Cataloged from student-submitted PDF of thesis.Includes bibliographical references (pages 81-84).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.