# Secure multiparty computations in floating-point arithmetic

arXiv: https://arxiv.org/abs/2001.03192 · HTML: https://mlsanity.com/paper/2001.03192/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2020.

## Built on by (most cited first, 8 of 8)

- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [Byzantine-Robust and Privacy-Preserving Framework for FedML](https://mlsanity.com/paper/2105.02295/) (2021)
- [Privacy-Preserving Feature Selection with Secure Multiparty Computation](https://mlsanity.com/paper/2102.03517/) (2021)
- [Secure Multiparty Computation for Synthetic Data Generation from Distributed Data](https://mlsanity.com/paper/2210.07332/) (2022)
- [Privacy-Preserving Training of Tree Ensembles over Continuous Data](https://mlsanity.com/paper/2106.02769/) (2021)
- [Training Differentially Private Models with Secure Multiparty Computation](https://mlsanity.com/paper/2202.02625/) (2022)
- [Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems](https://mlsanity.com/paper/2212.06264/) (2022)
- [Privacy and Integrity Preserving Training Using Trusted Hardware](https://mlsanity.com/paper/2105.00334/) (2021)

## Abstract (arXiv)

Secure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data while being unable to glean much information about the data (at least not without collusion among all parties to put back together all the shares). Thus, the parties may conspire to send all their processed results to a trusted third party (perhaps the data provider) at the conclusion of the computations, with only the trusted third party being able to view the final results. Secure multiparty computations for privacy-preserving machine-learning turn out to be possible using solely standard floating-point arithmetic, at least with a carefully controlled leakage of information less than the loss of accuracy due to roundoff, all backed by rigorous mathematical proofs of worst-case bounds on information loss and numerical stability in finite-precision arithmetic. Numerical examples illustrate the high performance attained on commodity off-the-shelf hardware for generalized linear models, including ordinary linear least-squares regression, binary and multinomial logistic regression, probit regression, and Poisson regression.