Synthetic Data Privacy Metrics

TL;DR

This paper reviews various privacy metrics for synthetic data, emphasizing the need for standardization and discussing methods to improve privacy in generative models, crucial for secure AI applications.

Contribution

It provides a comprehensive review of existing privacy metrics and best practices for enhancing privacy in synthetic data generation, addressing the lack of standardization.

Findings

Analysis of pros and cons of popular privacy metrics

Discussion of adversarial attack simulations for privacy assessment

Overview of techniques like differential privacy for model enhancement

Abstract

Recent advancements in generative AI have made it possible to create synthetic datasets that can be as accurate as real-world data for training AI models, powering statistical insights, and fostering collaboration with sensitive datasets while offering strong privacy guarantees. Effectively measuring the empirical privacy of synthetic data is an important step in the process. However, while there is a multitude of new privacy metrics being published every day, there currently is no standardization. In this paper, we review the pros and cons of popular metrics that include simulations of adversarial attacks. We also review current best practices for amending generative models to enhance the privacy of the data they create (e.g. differential privacy).