Rishabh Poddar

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2020-211

December 17, 2020

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-211.pdf

A large number of services today are built around processing data that is collected from or shared by customers. While such services are typically able to protect the data when it is in transit or in storage using standard encryption protocols, they are unable to extend this protection to the data when it is being processed, making it vulnerable to breaches. This not only threatens data confidentiality in existing services, it also prevents customers from availing such services altogether for sensitive workloads, in that they are unwilling / unable to share their data out of privacy concerns, regulatory hurdles, or business competition.

Existing solutions to this problem are unable to meet the requirements of advanced data analysis applications. Systems that are efficient do not provide strong enough security guarantees, and approaches with stronger security are often not efficient.

To address this problem, the work in this dissertation develops new systems and protocols for securely computing on encrypted data, that attempt to bridge the gap between security and efficiency. We distill design principles based on the properties of the two primary approaches for secure computation—advanced cryptographic protocols and trusted execution environments. Informed by these principles, we design novel cryptographic protocols and algorithms with strong and provable security guarantees, using which we show how to build systems that are both secure and efficient.

Advisors: Raluca Ada Popa

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BibTeX citation:

@phdthesis{Poddar:EECS-2020-211,
    Author= {Poddar, Rishabh},
    Title= {Secure Computation Systems for Confidential Data Analysis},
    School= {EECS Department, University of California, Berkeley},
    Year= {2020},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-211.html},
    Number= {UCB/EECS-2020-211},
    Abstract= {A large number of services today are built around processing data that is collected from or shared by customers. While such services are typically able to protect the data when it is in transit or in storage using standard encryption protocols, they are unable to extend this protection to the data when it is being processed, making it vulnerable to breaches. This not only threatens data confidentiality in existing services, it also prevents customers from availing such services altogether for sensitive workloads, in that they are unwilling / unable to share their data out of privacy concerns, regulatory hurdles, or business competition.

Existing solutions to this problem are unable to meet the requirements of advanced data analysis applications. Systems that are efficient do not provide strong enough security guarantees, and approaches with stronger security are often not efficient.

To address this problem, the work in this dissertation develops new systems and protocols for securely computing on encrypted data, that attempt to bridge the gap between security and efficiency. We distill design principles based on the properties of the two primary approaches for secure computation—advanced cryptographic protocols and trusted execution environments. Informed by these principles, we design novel cryptographic protocols and algorithms with strong and provable security guarantees, using which we show how to build systems that are both secure and efficient.},
}

EndNote citation:

%0 Thesis
%A Poddar, Rishabh 
%T Secure Computation Systems for Confidential Data Analysis
%I EECS Department, University of California, Berkeley
%D 2020
%8 December 17
%@ UCB/EECS-2020-211
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-211.html
%F Poddar:EECS-2020-211