MPC for MPC: Secure Computation on a Massively Parallel Computing Architecture
Abstract
Massively Parallel Computation (MPC) is a model of computation widely believed to best capture realistic parallel computing architectures such as large-scale MapReduce and Hadoop clusters. Motivated by the fact that many data analytics tasks performed on these platforms involve sensitive user data, we initiate the theoretical exploration of how to leverage MPC architectures to enable efficient, privacy-preserving computation over massive data. Clearly if a computation task does not lend itself to an efficient implementation on MPC even without security, then we cannot hope to compute it efficiently on MPC with security. We show, on the other hand, that any task that can be efficiently computed on MPC can also be securely computed with comparable efficiency.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 12, 2020
- Accession Number
- AD1198043
Entities
People
- Elaine Shi
- Kai-Min Chung
- T.-h. Hubert Chan
- Wei-kai Lin
Organizations
- Academia Sinica
- Cornell University