Practical considerations on using private sampling for synthetic data

TL;DR

This paper examines private sampling for generating differentially private synthetic data, highlighting implementation challenges and discussing the practicality of its constraints in real-world scenarios.

Contribution

It provides an implementation of the private sampling algorithm and critically analyzes the realism of its constraints for practical applications.

Findings

Implementation of private sampling demonstrated

Constraints may be unrealistic for real datasets

Discussion on practical applicability of the method

Abstract

Artificial intelligence and data access are already mainstream. One of the main challenges when designing an artificial intelligence or disclosing content from a database is preserving the privacy of individuals who participate in the process. Differential privacy for synthetic data generation has received much attention due to the ability of preserving privacy while freely using the synthetic data. Private sampling is the first noise-free method to construct differentially private synthetic data with rigorous bounds for privacy and accuracy. However, this synthetic data generation method comes with constraints which seem unrealistic and not applicable for real-world datasets. In this paper, we provide an implementation of the private sampling algorithm and discuss the realism of its constraints in practical cases.