{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/129840"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/129840","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Tiresias : a peer-to-peer platform for privacy preserving machine learning","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.","abstract_html":"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&#x27; 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.","abstract_has_math":false,"creators":["Zhang, Kevin,M. Eng.Massachusetts Institute of Technology."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","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":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-22T22:20:51Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/129840","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kalyan Veeramachaneni."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Zhang, Kevin,M. Eng.Massachusetts Institute of Technology."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-02-19T20:12:34Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-02-19T20:12:34Z"]},{"key":"dc:date.issued","label":"Date","values":["2020"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["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."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/129840"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Tiresias : a peer-to-peer platform for privacy preserving machine learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kalyan Veeramachaneni."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Zhang, Kevin,M. Eng.Massachusetts Institute of Technology."],"dc:date.accessioned":["2021-02-19T20:12:34Z"],"dc:date.available":["2021-02-19T20:12:34Z"],"dc:date.issued":["2020"],"dc: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)."],"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."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/129840"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Tiresias : a peer-to-peer platform for privacy preserving machine learning"],"dc:type":["Thesis"],"thesis:degree_name":["Master"]},"updated_at":"2026-07-22T22:20:51Z"}