Author
Kennard, Lindsey
Other Contributors
Milanova, Ana; Turner, Wes; Yener, Bülent, 1959-; Krishnamoorthy, M. S.; Zikas, Vassilis;
Date Issued
2020-12
Subject
Computer science
Degree
PhD;
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
Abstract
The world of large scale secure computation has changed. Computational needs have far outpaced the ability of most modern companies to build
and maintain their own datacenters. Cloud computation has picked up this
slack and provided very advanced frameworks that allow customers to,
with the click of a button, create relatively low-cost solutions
that can be customized to handle almost any conceivable task.
While cloud computing certainly solves the scalability issue, it introduces
a huge problem: program security.; MPC Amortization is a novel algorithm to better utilize parallelization and amortizationin loops scheduled by MPC compilers. As MPC compilation gains popularity
results from traditional compiler research can be leveraged to
MPC's specific requirements. Our analysis considers the problem from a
program analysis/compilers point of view. It casts the problem in terms of
a known NP-hard problem: Shortest Common Supersequence, and presents a
scheduling algorithm as well as reasoning about the optimality of schedules.
We apply our scheduling algorithm on loops from the literature and present
our results.; SecureMCMR utilizes two non-colluding clouds to execute MapReduce programsover encrypted data. One cloud executes the MapReduce program over encrypted data.
When it encounters unsupported operations, it sends certain data to the second cloud and the two
clouds compute the operation collaboratively. Neither cloud has the ability to view the original input data, and all
data that is sent between the clouds are `blinded' hiding their original
values. We also present security classification for MapReduce programs
and results of running MapReduce applications under SecureMCMR on two clouds: Google and AWS.; Customers often must choose between the flexibility of cloud solutionsand the security of using either self hosted or ``made to order'' secure
setups. Still worse, for some customers, especially those
working on classified projects, public cloud solutions cannot be used at all.
This disconnect has created a demand for a solution that provides security, scalability
and cusoimization while still being relatively low cost. In this Thesis
we present work on two problems from secure computational. The first,
SecureMCMR uses Partially Homomorphic Encryption (PHE) to run MapReduce programs
more securely on the cloud. The second, MPC Amortization, is a new scheduling
algorithm to improve the performance of loops in Secure Multipartry Computation
(MPC) compilers.;
Description
December 2020; School of Science
Department
Dept. of Computer Science;
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection;
Access
Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.;