Distributed and Private Computation for Inference
Name
singh-abhishek-SM-MAS-2021-thesis.pdf
Description
Thesis PDF
Size
15.47 MB
Format
Adobe PDF
Checksum (MD5)
8a6d6a4c0eed17c5b038593b8cdc71c1
Author(s)
Singh, Abhishek
Advisor(s)
Raskar, Ramesh
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
Abstract
Recent progress in mobile and cloud computing coupled with the increase in data has resulted in a data-driven ecosystem that is making an impact in several domains of science and engineering. However, this data-driven ecosystem lacks protective measures for privacy resulting in regulations and behaviors that restrict data sharing. Augmenting the existing data-driven ecosystem with privacy preserving solutions could unlock the access to data silos, increasing the impact manifold. In this thesis, I discuss and identify gaps in some of the existing works and develop privacy preserving mechanisms for data analysis and distributed computation. At an abstract level, existing work in this domain includes federated learning, differential privacy, and encrypted computations. I describe the practical scenarios where all these approaches do not suffice due to their intrinsic computation infeasibility or suboptimal privacy-utility trade-off. This work augments such existing approaches by improving certain trade-offs and utilizing priors specific to the problem.
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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