2026-09-19T05:39:18Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1536852024-03-14T03:21:58Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Raskar, Ramesh
Vepakomma, Praneeth
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2024-03-13T13:26:30Z
2024-03-13T13:26:30Z
2024-02
2024-02-14T18:54:48.800Z
https://hdl.handle.net/1721.1/153685
Data in today’s world is increasingly siloed across a wide variety of entities with varying resource constraints. The quality of wisdom generated from a collaborative processing of such data is substantially better if the data from all these entities is shared across each other or centralized at a nodal entity. Such data sharing and centralization is often prohibited due to stringent privacy regulations, computational constraints, communication bottlenecks, trade secrets, trust issues and competition. This necessitates development of efficient methods for distributed computation while preserving privacy to generate wisdom whose quality is on par with the case of data centralization. This thesis covers methods introduced for the same in an inter-disciplinary manner to tackle several such problems using distributed and private computation.
Ph.D.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Connecting silos with distributed and private computation
Thesis
application/pdf
Doctoral
Doctor of Philosophy
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="b6ab220e-21af-42e1-a653-433531eb6de6">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>Connecting silos with distributed and private computation</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2024-02</PublicationDate>
<Authors>
<Author>
<DisplayName>Vepakomma, Praneeth</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>https://rightsstatements.org/page/InC-EDU/1.0/</License>
<Abstract>Data in today’s world is increasingly siloed across a wide variety of entities with varying resource constraints. The quality of wisdom generated from a collaborative processing of such data is substantially better if the data from all these entities is shared across each other or centralized at a nodal entity. Such data sharing and centralization is often prohibited due to stringent privacy regulations, computational constraints, communication bottlenecks, trade secrets, trust issues and competition. This necessitates development of efficient methods for distributed computation while preserving privacy to generate wisdom whose quality is on par with the case of data centralization. This thesis covers methods introduced for the same in an inter-disciplinary manner to tackle several such problems using distributed and private computation.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>