differential privacy

Risk-Equalized Privacy: Protecting Outliers in Synthetic Data

New research introduces risk-equalized differentially private synthetic data that protects outliers by controlling record-level influence, addressing critical privacy gaps in AI training data.

synthetic data

PRISM: Structure-Aware Privacy Budgets for Synthetic Data

New research introduces PRISM, a differentially private synthetic data framework using structure-aware budget allocation to optimize prediction accuracy while maintaining privacy guarantees.

synthetic data

GEM+ Framework Advances Private Synthetic Data Generation

Researchers introduce GEM+, a scalable framework for generating privacy-preserving synthetic data using generator networks. The approach addresses differential privacy while maintaining data utility for machine learning applications.