Secure Computation for Machine Learning With SPDZ
Abstract
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. We demonstrate that the SPDZ framework outperforms these previous implementations while providing stronger security.
- Publication:
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arXiv e-prints
- Pub Date:
- January 2019
- DOI:
- 10.48550/arXiv.1901.00329
- arXiv:
- arXiv:1901.00329
- Bibcode:
- 2019arXiv190100329C
- Keywords:
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- Computer Science - Cryptography and Security
- E-Print:
- 32nd Conference on Neural Information Processing Systems (NIPS 2018)