# Secure Computation for Machine Learning With SPDZ

arXiv: https://arxiv.org/abs/1901.00329 · HTML: https://mlsanity.com/paper/1901.00329/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2019.

## Built on by (most cited first, 5 of 5)

- [SIRNN: A Math Library for Secure RNN Inference](https://mlsanity.com/paper/2105.04236/) (2021)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [FedST: Secure Federated Shapelet Transformation for Time Series Classification](https://mlsanity.com/paper/2302.10631/) (2023)
- [ERIS: Enhancing the Privacy-Utility-Scalability Trade-Off in Federated Learning via Federated Shard Aggregation](https://mlsanity.com/paper/2602.08617/) (2026)
- [Order Optimal Bounds for One-Shot Federated Learning over non-Convex Loss Functions](https://mlsanity.com/paper/2108.08677/) (2021)

## Abstract (arXiv)

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.