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Secure Computation for Machine Learning With SPDZ · Around

2019

Secure Computation for Machine Learning With SPDZ

Chen, Valerie, Pastro, Valerio, Raykova, Mariana

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Understand

Secure Multi-Party Computation (MPC) is an area of cryptography that enables computation on sensitive data from multiple sources while maintaining privacy guarantees.

However, theoretical MPC protocols often do not scale efficiently to real-world data.
This project investigates the efficiency of the SPDZ framework, which provides an implementation of an MPC protocol with malicious security, in the context of popular machine learning (ML) algorithms.
In particular, we chose applications such as linear regression and logistic regression, which have been implemented and evaluated using semi-honest MPC techniques.

Built on

The elements of statistical learning: data mining, inference, and prediction, 2nd Edition
Trevor Hastie, Robert Tibshirani, and Jerome H. Friedman · 2009
Earlier work this paper cites.
Foundations of Cryptography: Volume 2, Basic Applications
Goldreich Oded · 2009
Earlier work this paper cites.
A note on the relation between the definitions of security for semi-honest and malicious adversaries ?, 2010
Carmit Hazay and Yehuda Lindell · 2010
Earlier work this paper cites.

Similar

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Obliv-c: A language for extensible data-oblivious computation
Samee Zahur and David Evans · 2015
Cited alongside, same era.

Then

Privacy-preserving distributed linear regression on high-dimensional data
Adrià Gascón, Phillipp Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahur, and David Evans · 2017
Later among the works it cites.
Secureml: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang · 2017
Later among the works it cites.
Overdrive: Making SPDZ great again
Marcel Keller, Valerio Pastro, and Dragos Rotaru · 2018
Later among the works it cites.

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