# Secure Multiparty Computation for Synthetic Data Generation from Distributed Data

arXiv: https://arxiv.org/abs/2210.07332 · HTML: https://mlsanity.com/paper/2210.07332/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Differentially Private Generative Adversarial Network](https://mlsanity.com/paper/1802.06739/) (2018)
- [DP-CGAN: Differentially Private Synthetic Data and Label Generation](https://mlsanity.com/paper/2001.09700/) (2020)
- [Synthetic Data for Deep Learning](https://mlsanity.com/paper/1909.11512/) (2019)
- [Winning the NIST Contest: A scalable and general approach to differentially private synthetic data](https://mlsanity.com/paper/2108.04978/) (2021)
- [Differentially Private Synthetic Data: Applied Evaluations and Enhancements](https://mlsanity.com/paper/2011.05537/) (2020)
- [Secure Quantized Training for Deep Learning](https://mlsanity.com/paper/2107.00501/) (2021)
- [Protecting Privacy of Users in Brain-Computer Interface Applications](https://mlsanity.com/paper/1907.01586/) (2019)
- [Privacy-Preserving Video Classification with Convolutional Neural Networks](https://mlsanity.com/paper/2102.03513/) (2021)
- [High Performance Logistic Regression for Privacy-Preserving Genome Analysis](https://mlsanity.com/paper/2002.05377/) (2020)
- [Secure multiparty computations in floating-point arithmetic](https://mlsanity.com/paper/2001.03192/) (2020)
- [FedSyn: Synthetic Data Generation using Federated Learning](https://mlsanity.com/paper/2203.05931/) (2022)
- [Privacy-Preserving Training of Tree Ensembles over Continuous Data](https://mlsanity.com/paper/2106.02769/) (2021)

## Built on by (most cited first, 6 of 6)

- [NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes](https://mlsanity.com/paper/2310.15959/) (2023)
- [README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP](https://mlsanity.com/paper/2312.15561/) (2023)
- [Synthetic Tabular Data: Methods, Attacks and Defenses](https://mlsanity.com/paper/2506.06108/) (2025)
- [FLAIM: AIM-based Synthetic Data Generation in the Federated Setting](https://mlsanity.com/paper/2310.03447/) (2023)
- [Scalable and Privacy-Preserving Synthetic Data Generation on Decentralised Web](https://mlsanity.com/paper/2310.20062/) (2023)
- [EHR Interaction Between Patients and AI: NoteAid EHR Interaction](https://mlsanity.com/paper/2312.17475/) (2023)

## Abstract (arXiv)

Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education. Synthetic data generation algorithms with privacy guarantees are emerging as a paradigm to break this data logjam. Existing approaches, however, assume that the data holders supply their raw data to a trusted curator, who uses it as fuel for synthetic data generation. This severely limits the applicability, as much of the valuable data in the world is locked up in silos, controlled by entities who cannot show their data to each other or a central aggregator without raising privacy concerns. To overcome this roadblock, we propose the first solution in which data holders only share encrypted data for differentially private synthetic data generation. Data holders send shares to servers who perform Secure Multiparty Computation (MPC) computations while the original data stays encrypted. We instantiate this idea in an MPC protocol for the Multiplicative Weights with Exponential Mechanism (MWEM) algorithm to generate synthetic data based on real data originating from many data holders without reliance on a single point of failure.