Tiresias : a peer-to-peer platform for privacy preserving machine learning
Name
1237567840-MIT.pdf
Size
4.64 MB
Format
Adobe PDF
Checksum (MD5)
2309b98af3db2aafaba79527f7360d65
Author(s)
Zhang, Kevin,M. Eng.Massachusetts Institute of Technology.
Advisor(s)
Kalyan Veeramachaneni.
Alternative Title
Peer-to-peer platform for privacy preserving machine learning
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 81-84).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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