Secure Multiparty Computation for Synthetic Data Generation from Distributed Data

Mayana Pereira, Sikha Pentyala, Martine De Cock, Anderson Nascimento, Rafael de Sousa

Keywords: Synthetic Datasets, Differential Privacy, Secure Multiparty Computation

TL;DR: We propose the first solution for distributed differentially private synthetic data generation protocol that does not rely on a single trusted party.

Abstract:

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.