REVIEW 2 cited by
Synthetic Data Privacy Metrics
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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).
Forward citations
Cited by 2 Pith papers
-
Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes
ReFine combines rule-guided prompting and dual-granularity filtering to improve LLM-based tabular data generation when only 30 to 90 labeled rows exist, achieving top average rank over baselines.
-
Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGN
TabularARGN is a discretization-based auto-regressive network claimed to generate high-fidelity, privacy-robust synthetic tabular data, competitive with diffusion and GAN baselines.
Discussion (0). Sign in to comment.