Connecting silos with distributed and private computation
Name
vepakomma-praneeth-PHD-MAS-2024-thesis.pdf
Description
Thesis PDF
Size
24.3 MB
Format
Adobe PDF
Checksum (MD5)
38b87ac9af3bc4e750253f0d6d5d54a7
Author(s)
Vepakomma, Praneeth
Advisor(s)
Raskar, Ramesh
Date Issued
February 2024
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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