End to End Collaborative Synthetic Data Generation

TL;DR

This paper introduces an end-to-end collaborative framework for generating and publishing synthetic data that preserves privacy, enabling multiple data custodians to collaboratively produce useful synthetic datasets, exemplified with genomic data for leukemia.

Contribution

It presents a novel end-to-end framework for collaborative synthetic data generation that includes preprocessing, hyperparameter tuning, and evaluation, using secure multiparty computation protocols.

Findings

Framework effectively enables privacy-preserving collaborative data sharing.

Successful application to synthetic genomic data for leukemia.

Demonstrates feasibility of end-to-end synthetic data pipeline.

Abstract

The success of AI is based on the availability of data to train models. While in some cases a single data custodian may have sufficient data to enable AI, often multiple custodians need to collaborate to reach a cumulative size required for meaningful AI research. The latter is, for example, often the case for rare diseases, with each clinical site having data for only a small number of patients. Recent algorithms for federated synthetic data generation are an important step towards collaborative, privacy-preserving data sharing. Existing techniques, however, focus exclusively on synthesizer training, assuming that the training data is already preprocessed and that the desired synthetic data can be delivered in one shot, without any hyperparameter tuning. In this paper, we propose an end-to-end collaborative framework for publishing of synthetic data that accounts for privacy-preserving preprocessing as well as evaluation. We instantiate this framework with Secure Multiparty Computation (MPC) protocols and evaluate it in a use case for privacy-preserving publishing of synthetic genomic data for leukemia.