Synthetic Data Privacy Metrics

2025-01-07

AI Summary

Current privacy evaluation of synthetic data lacks standardized benchmarks, and existing metrics inadequately capture real-world adversarial risks. Method: This paper presents the first systematic empirical evaluation of mainstream privacy metrics—including adversarial attack simulation and membership inference success rates—in generative models. It comparatively analyzes practical efficacy of privacy-enhancing techniques such as differential privacy integration and privacy-aware generation, and quantifies the privacy–utility trade-off. Contribution/Results: We propose a deployment-oriented synthetic data privacy assessment framework featuring a reproducible evaluation pipeline, a standardized metric suite, and implementation guidelines. The study establishes a rigorous benchmarking methodology for academia and delivers an actionable, practice-driven privacy assurance evaluation paradigm—with concrete best practices—for industry adoption.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Generation
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

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).

Problem

• Privacy Protection
• Synthetic Data
• Evaluation Criteria

Innovation

• Privacy Protection
• Differential Privacy
• Synthetic Data Evaluation