Secure parallel computation on national scale volumes of data

Authors: 

Sahar Mazloom and Phi Hung Le, George Mason University; Samuel Ranellucci, Unbound Tech; S. Dov Gordon, George Mason University

Abstract: 

We revisit the problem of performing secure computation of graph-parallel algorithms, focusing on the applications of securely outsourcing matrix factorization, and histograms. Leveraging recent results in low-communication secure multi-party computation, and a security relaxation that allows the computation servers to learn some differentially private leakage about user inputs, we construct a new protocol that reduces overall runtime by 320X, reduces the number of AES calls by 750X , and reduces the total communication by 200X . Our system can securely compute histograms over 300 million items in about 4 minutes, and it can perform sparse matrix factorization, which is commonly used in recommendation systems, on 20 million records in about 6 minutes. Furthermore, in contrast to prior work, our system is secure against a malicious adversary that corrupts one of the computing servers.

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BibTeX
@inproceedings {247676,
author = {Sahar Mazloom and Phi Hung Le and Samuel Ranellucci and S. Dov Gordon},
title = {Secure parallel computation on national scale volumes of data},
booktitle = {29th {USENIX} Security Symposium ({USENIX} Security 20)},
year = {2020},
isbn = {978-1-939133-17-5},
pages = {2487--2504},
url = {https://www.usenix.org/conference/usenixsecurity20/presentation/mazloom},
publisher = {{USENIX} Association},
month = aug,
}

Presentation Video