dc.contributor.advisor | Kalyan Veeramachaneni. | en_US |
dc.contributor.author | Zhang, Kevin,M. Eng.Massachusetts Institute of Technology. | en_US |
dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
dc.date.accessioned | 2021-02-19T20:12:34Z | |
dc.date.available | 2021-02-19T20:12:34Z | |
dc.date.copyright | 2020 | en_US |
dc.date.issued | 2020 | en_US |
dc.identifier.uri | https://hdl.handle.net/1721.1/129840 | |
dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020 | en_US |
dc.description | Cataloged from student-submitted PDF of thesis. | en_US |
dc.description | Includes bibliographical references (pages 81-84). | en_US |
dc.description.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. | en_US |
dc.description.statementofresponsibility | by Kevin Zhang. | en_US |
dc.format.extent | 84 pages | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | 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. | en_US |
dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Electrical Engineering and Computer Science. | en_US |
dc.title | Tiresias : a peer-to-peer platform for privacy preserving machine learning | en_US |
dc.title.alternative | Peer-to-peer platform for privacy preserving machine learning | en_US |
dc.type | Thesis | en_US |
dc.description.degree | M. Eng. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.identifier.oclc | 1237567840 | en_US |
dc.description.collection | M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science | en_US |
dspace.imported | 2021-02-19T20:12:03Z | en_US |
mit.thesis.degree | Master | en_US |
mit.thesis.department | EECS | en_US |