Parallel and Cloud-Native Secure Multi-Party Computation
Date
2022
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Secure multi-party computation (MPC) enables analysis based on sensitive data from
multiple data owners, applying distributed cryptographic protocols to ensure privacy.
Such protocols introduce distinct communication requirements, causing the computation
to run significantly longer than its counterpart, conventional computing. General
MPC frameworks are available that make it simple to develop such privacy-preserving
applications, but running said applications assumes multiple non-colluding computing
parties that host the protocol runtimes, having rigorously set up the required infrastructure.
Utilising cloud resources for this occasion is a good alternative to on-premises
deployments. First, it allows for a larger degree of automation in the infrastructure
set-up. Secondly, cloud datacenters enjoy superior network characteristics, detrimental
for MPC performance, and offer elastic compute resources at competitive price models.
This thesis presents a cloud-native deployment of the SHAREMIND MPC framework on
Kubernetes. It further proposes methods for parallel programming, with which MPC
applications could be scaled over clusters. Familiar programming models, MapReduce
and bulk-synchronous parallel, are adapted to MPC, and benchmarked in commodity
clouds, showing near-linear speedup.
Description
Keywords
secure multi-party computation, parallel computation, cloud-native applications